{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #from WA 7/15/24 - orig AZ based on blockgroups (?)\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - DO WE NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ??  #\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections count only if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, borderUnits, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     new for MN 4/7/24 - don't enforce contiguity here -- too slow -- fix in cleanup stage instead\n",
    "     \"\"\"\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        tryList = getAdjoiners(addedList,unitNbrs)\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP :  #and wontEnclave(UU, addedList, unitNbrs, borderUnits) :  #we'll post-fix contig'y\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  AZ\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use az_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. AZ has 7151502 peeps and is divided into 1538 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 9 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 15 counties in AZ .  I will assign them county Nos 0 - 14\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 1 unpopulated coastal tracts.\n",
      "Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.\")\n",
    "if len(skipList) > 0:\n",
    "    print(\"Lets plot them and their parent counties\")\n",
    "    for t in skipList:\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(t,tractGeom[t],6)\n",
    "    plotPoly(origMAP,0.2)\n",
    "    for c in range(nCounties):\n",
    "        if isAlteredCounty[c]:\n",
    "            plotPoly(countyGeom[c])\n",
    "    plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 1 units will be axed\n",
      "here is the proposed skipList [440]\n",
      "here is the final skipList []\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "skipList = [] #...  #keep in for smooth SW boundary\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 14\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excellent!  We have no populated islands.  SKIP the below code block.\n",
      "we will scale longitudinal distance by 0.82588\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 3739 is a true island.  We will move it to adjoin the mainland in same county.\n",
      "unit 3759 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 3068 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 3097 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1006 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGdCAYAAAAfTAk2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABv3UlEQVR4nO3dd3wU1d4G8Ge2pm56JRVCCYQSehNBQEBQVNQrRhELKteGvl4VrteuqLfZpVzF3kXFAkpHpBoIhBZIQkjvve1md+f9Y5OFmIS02czu5vl+3E+SmdnZ35Ek++TMmXMEURRFEBERETkRhdwFEBEREUmNAYeIiIicDgMOEREROR0GHCIiInI6DDhERETkdBhwiIiIyOkw4BAREZHTYcAhIiIip6OSu4CeYjabkZubC09PTwiCIHc5RERE1AGiKKKqqgqhoaFQKDreL9NrAk5ubi7Cw8PlLoOIiIi6ICsrC2FhYR0+vtcEHE9PTwCW/0E6nU7maoiIiKgjKisrER4ebn0f76heE3CaLkvpdDoGHCIiIgfT2eElHGRMRERETocBh4iIiJwOAw4RERE5HQYcIiIicjoMOEREROR0GHCIiIjI6TDgEBERkdNhwCEiIiKnw4BDRERETocBh4iIiJwOAw4RERE5HQYcIiIicjoMOERERLZmqAUKTgCiKHclvQYDDhERka0YDUDuYWDtNOCdCcDPjzDk9BAGHCIiIlvYvxp4KRxYMxVoqAPG3QMc/B+Q9InclfUKKrkLICIichqVucCmxwGNJ5D0MTDiZiDuWiD6UkCpAgpPAIc/BuJvlrtSp8eAQ0REJJXtLwInvrd8HhALzPsvoNJYvjabgNwkYOwS2crrTRhwiIiIpFCeBRz5DLj8eWDw1YDG/Xy4AYDqQkBfCYSNla3E3oQBh4iISAq/vwpodcCo2wCtR8v9lbmWj7qQHi2rt+IgYyIiou4qTQcOfQRMuLf1cAMA2QcBQQn49u3Z2nop9uAQERF1RX0FcPJHIPkr4OxOwM2/7fE1+mrgt38Dg+cDWs+erbOXYsAhIiLqKKMeOPOrJdSkbAJMBiBqsmUw8eD5gItX689L2wbUFAKXPdGz9fZiDDhEREStMTUAtSWAewCQsdsSak5sAPQVQPAwS1iJWwB49Wn/XOd+B3yiAb9+tq+bADDgEBERtVRdBHx8DZCfbBk3I5oAnyhg3F3A0OuBgIGdO1/+MSBkuE1KpdYx4BAREV3IaAC+SACqCoAJ9wGiGRhyLRA2GhCEzp/PZLQEpUn3S18rtYkBh4iIqEltqWW9qNzDwOKfgfAx3T9n5h7LZa1+l3X/XNRhDDhERET6auCX5cDRLy09NteukSbcmM3A1ucssxqHxHf/fNRhDDhERESbHgeOrQcufRSIXwR4BEhz3q9uBbIPAIs2AApOPdeTGHCIiKh3K2hcAPOKf0q7TlT6DuDkBmD2y0DfS6U7L3UI4yQREfVuJzcALjpg1GLpzllbCnx1m2UVcS6uKQsGHCIi6t1St1qCiFLd9XPUVwCbn7RM6AdYQlN9hWUsj0IpTZ3UKQw4RETk9Hbt2oUrr7wSoaGhEAQB3333nWVHXTmQ8wfEfpfhySefREhICFxdXTFjxgycOXOm2TlKS0uRkJAAnU4Hb29v3HHHHaiurrYMJF5/N/D7axA/S8C/nl2OAfMfhva5cvQZNAovvPBCj7eXGHCIiKgXqKmpwfDhw/HWW28133F2JyCa8cqPp/H6669j1apV2L9/P9zd3TFr1izU19dbD01ISMDx48exefNm/Pjjj9i1axfuuusu4OjnwOmNwIJ38eCvDfjf26/jX1cG4dTKqdiwYQPGjh3bw60lgIOMiYioF5gzZw7mzJnTckfaNoi+MXh15ft44oknMH/+fADAhx9+iKCgIHz33Xe48cYbcfLkSWzatAkHDx7E6NGjAQBvvPEGrrjiCvyr3z6EDrsWJ1VD8M7echx7OAIDXXOBifcgetSonmwmXYA9OERE1DuJIpC6DWc9RiM/Px8zZsyw7vLy8sK4ceOwd+9eAMDevXvh7e1tDTcAMGPGDCgEYH9aKTDzWfzwww/o27cffgz4K6LXeSEq4b+48847UVpa2uNNo24GnJdeegmCIGDZsmXWbWvWrMHUqVOh0+kgCALKy8s7dK6cnBzcfPPN8PPzg6urK4YOHYo//vjDun/x4sUQBKHZY/bs2d0pn4iIerPcQ0BFJvI9BgMAgoKCmu0OCgpCfn4+ACA/Px+BgYHN9qtUKvi6CsjXRAPe4UhPT8e5c+fw1ddf48OPP8H777+PxMREXHfddT3THmqmy5eoDh48iNWrV2PYsGHNttfW1mL27NmYPXs2li9f3qFzlZWVYdKkSZg2bRo2btyIgIAAnDlzBj4+Ps2Omz17NtatW2f9WqvVdrV8IiLq7RI/AHRhQOjIrp9DobLeJWU2m6HX6/Hhhx9iwIABAIB3330Xo0aNQkpKCgYO7OQCndQtXQo41dXVSEhIwNq1a/H8888329fUm7Njx44On+/ll19GeHh4s/ASHR3d4jitVovg4OCulExERHReQx1w5htgwn0IDu0DACgoKEBISIj1kIKCAowYMQIAEBwcjMLCwmanMBoMKK0xIDg6FgAQEhIClUplDTcAEBtr2ZeZmcmA08O6dInq3nvvxdy5c5tdr+yODRs2YPTo0bj++usRGBiI+Ph4rF27tsVxO3bsQGBgIAYOHIilS5eipKSkzXPq9XpUVlY2exAREQEAMn4DDDVAfAKio6MRHByMrVu3WndXVlZi//79mDBhAgBgwoQJKC8vR2JiovWYbd99DLMZGDfDMjB50qRJMBqNSEtLsx5z+vRpAEBkZGRPtIou0OmA8/nnn+PQoUNYuXKlZEWkp6fjnXfeQf/+/fHLL79g6dKleOCBB/DBBx9Yj5k9ezY+/PBDbN26FS+//DJ27tyJOXPmwGQytXrOlStXwsvLy/oIDw+XrF4iInIs1dXVSEpKQlJSEgDg7O6vkeQ5HZmVsI4lff7557FhwwYkJydj0aJFCA0NxdVXXw3A0hMze/ZsLFmyBAcOHMDvv/+O+x5/GjfGqRDaLw6AZdDxyJEjcfvtt+Pw4cNITEzE3XffjZkzZzbr1aEeInZCZmamGBgYKB45csS67dJLLxUffPDBFsdu375dBCCWlZW1e161Wi1OmDCh2bb7779fHD9+fJvPSUtLEwGIW7ZsaXV/fX29WFFRYX1kZWWJAMSKiop26yEiIufS9J7058ett94qiqIoms1m8R//+IcYFBQkarVacfr06WJKSoooiqJ47YIF4pjxE8S0tDRx4cKFooeHh6jT6cTb/jJfrFruKYpZf1hfJycnR7z22mtFDw8PMSgoSFy8eLFYUlIiR5OdRkVFRZfevzs1BicxMRGFhYUYOfL8gCyTyYRdu3bhzTffhF6vh1LZ+SmpQ0JCMHjw4GbbYmNj8c0337T5nL59+8Lf3x+pqamYPn16i/1arZaDkImICAAwdepUiKJo+eKLW4CCY8B9idYVvgVBwLPPPotnn322xXPXN74XlZSU4NNPPz2/o74SeCkcKE4Bwizz3YSGhl70vYt6TqcCzvTp05GcnNxs22233YZBgwbhscce61K4ASzXLVNSUpptO3369EWvWWZnZ6OkpKTZgDAiIqKLKjoNnPwBmPdfa7hpj79/AIqLiyzLMlzIRQf4DwByDgEjbrJBsdQdnQo4np6eiIuLa7bN3d0dfn5+1u35+fnIz89HamoqACA5ORmenp6IiIiAr68vAEtQuuaaa3DfffcBAB566CFMnDgRL774Im644QYcOHAAa9aswZo1awBYrp0+88wzWLBgAYKDg5GWloZHH30UMTExmDVrVvf+DxARUe+x+z+AV1inAklwcHDrAQcAXLwAY52EBZJUJJ/JeNWqVYiPj8eSJZbl4adMmYL4+Hhs2LDBekxaWhqKi4utX48ZMwbffvstPvvsM8TFxeG5557Dq6++ioSEBACAUqnE0aNHcdVVV2HAgAG44447MGrUKPz222+8DEVERB1XeBLodxmg6vh7R2io5UpBqwGnoR5QuUhVHUmo22tR/Xm+m6effhpPP/30RZ+TkZHRYtu8efMwb968Vo93dXXFL7/80sUKiYiIGuUlAQXHgate7/BTmmYwrqqqarnTUMWAY6e4FhUREfUuGjfA3PoUI63x9PQE0EoPjigClXmAF6chsUcMOERE1HvcvN5y99OWpy0BpQM8PDwAtBJwqgsAkx7wDGrlWSQ3BhwiIuo9YqYDM54C9rwOJH/doZ6cNntwjq23rEUVfaktKqVuYsAhIqLeZdIyIOoSYP2dwCfXt9uT09SD02wMTuIHwJangMFXA26+tquVuowBh4iIehdBAG7+Bpj/FpC2FTj+7UUPbwo41jUN07YDPzwAxN8MXP22raulLmLAISKi3keltQSUgVcA25676KFNAce6mviJ7wHffsDc/3TqdnPqWQw4RETUewUNscxlcxFNY3Byc/OA498ByV9ZxvIIQg8USF3FgENERL1XeRbg1eeihzT14KAqD/j6diBqMjDjadvXRt3CgENERL1XSSrgF3PRQ5oCzqVBVZZem2vXABr3nqiOuoEBh4iIeidRBIpPAwEDL3qYh4cHlAJwyzA1ED7Osv4U2T0GHCIi6p3qywF9JeATfdHDPD098ew0Lcb0UQLTVvRMbdRtDDhERNQ71ZVZPrYzj413wV6suESLFVv1lvE35BAYcIiIqHfSN85MfLHxNGYz3LYux4aUBvxzjwEGg6FnaqNuY8AhIqLeSaGyfDQ1tH1MSSqEmkK8ecASbGpqanqgMJICAw4REfVOhScsHy92F9Xx9YDaDfuyLWtWNVuugewaAw4REfVOWfsB376Au3/r+81mIPF9YPiNqGq8MtViwU2yWww4RETU++irLDMS95/V9jHZByyT+w29wbqJAcdxMOAQEVHvc/RLS8iZeH/bx5z4HvAIBsLHISgoGAADjiNhwCEiot4nJxEIHtr2Mg2iCJz8AYidBygUCAkJAcAxOI6EAYeIiHofQQAEZdv797wBVGQBcQsAACEh7MFxNAw4RETU+2h1gKGNsJL8NbD5H8Al/wdETgQA+PtbBiIz4DgOBhwiIqImOYnAd0uBYX8BLvuHdbOnpycABhxHwoBDRES9T3km4B7Qcvuuf1luHb/qTctlrEZNAYdjcBwHAw4REfUuoghkHwTCxjTfXpIGpGwExt0DqDTNdnl4eABgD44jYcAhIqLepSQVqC4AIsaf3yaKwE8PA97hlstTf8KA43hUchdARETUo07+AKjdgOgp57f9/hqQvgNI+AbQuLV4SlPAqaio6KEiqbsYcIiIqHfJPQyEjQaqC4HDH1suSxUkA5MfAvrPaPUpTWNwCgoKerJS6gYGHCIi6l0MNcDZXcAboyy9Nf1nAVMfAwbNa/MpTT04eXn5PVUldRMDDhER9S7h44CcP4BJD1oGFGvc232KNeDk59m6OpIIAw4REfUuUx8DpvwNUHT8PhvrGJzychsVRVLjXVRERNT7dCLcAOfH4JDjYMAhIiJqR1MPjhQq9BXYmrkVDeYGyc5JLTHgEDmBYzkVKKsxyF0GkdOSIuCcKTuDXdm7cLToKKb0mYIt57ZIUBm1hQGHyMFV1DXg2rf3YOZ/d2F7SqHc5RA5JXf38wOR9Xp9p59/MP8g1Ao1poRNwSVhl0CtVGNU0CgkFyVLWSZdgAGHyMHtSCmEwWRG/0AP3LbuIJ78/hjqDCa5yyJyKkql0vp5R2czNotmfHP6G2zN3AqzaEaUV1Sz/QU1BQjxCJGyTLoA76IipyWKImoMJlTXG1HXYEKdwYS6BhPqGz+vNzZ+bLBsN5kta+sJaPooQKkQoFYpoFEKUCsV1odGJUCjVEKtFKBSChAEAUrBcryi8aNaKcBFrYSrWgmFQoDBaEaDyfIwGM0wmMxoMInNv7ZuN6PBKJ7/vPHYpnOYRVheW6HAtpRCxPXR4dMl4/DxvnN4/qeT+D21GK/dGI+4Pl5y/zMQOZ3q6mr4+fm1e9wf+X9gZtRM6DS6VvdXGCow1HWo1OVRIwYcsmuiKKKuwYSKugaU1zagou78o7LxY1vbK+oaYDSL7b6GWinARaWEUilAFC2vKQKACJhE0Rou5KBUCNAoFVApL/ioUkCAAKPJDKNZhMks4v8uHwhBEHDLhChM6OeHZV8k4eq3fsfDlw/A3VP6QakQ2n8xIuqQjvTgiKKIqoaqNsMN2R4DDtmU2WwJKDUGI2r1lo/V9UaU1TagrNaAsloDymsbUFpjQGVdA/RGszWkVNZbQkpb4cJdo4SXqxo6VzW8XNXwdlMjSOcBr8avvVzV8HLTwFOrgqvG0pPS9FGrVsBVrYSLWgm1sv0rtaIonu9xaexF0Tf2tpjNIkyiJWiIImAyizCLlt6WeqMZdQYTzKIIjVIBtUoBtVKAVnVhb5DCsq/xc3Vjb5FGqYCiC8EkJtAT65dOwqtbTuOfv6Rgx6ki/PuG4Qj3bbm+DhF1nEKhgNlsRlVVVbvHHio8hJGBIy96jFk0S1UatYIBh1plNouoN5pQa7Bcxqk1mFBrMKJGb0JJjR4l1QaU1hisn1fVG6E3Nh1nObbp87YIAuDlqoaPmwbebmp4aFVw0ygRpPOAzqUpoKibhRgvVzW8G7/uSDCRiiAI0KqU0KoAaHvsZbtMo1Lg0dmDMHVgIB76IglXvPYbnpk/BNfE94EgsDeHqCuCg0OQm5tz0R4co9mIpMIk1Jvq4ePi0+Zx5yrPIcgtyBZlUiMGHCdgNovQG82orG9AelENXDVK6BtMqNYbUVVvRFV9A6oaP6/VNwaPhqbgYrwgwFjGotQajKhvuPhfFlqVAv4eWvh5aODrroG/pxYapQLuWiXcNCq4a5Rw0/7po0YFd60S7loVfNw08HJV89KJjY2N9sXGZZfg6e+P4+Evj2DrqUK8cHUcvN00cpdG5HCCg4NbDTiiKOJ4yXGU1JVAqVBiRMAIeGguflv52YqzmBo+1YbVEgOOnTGaLJdoyq1jSyyXcMprLdsqag3Ir6xHamE1csvrYWq8FHIxKoUATxcVPFxUcNdYekksl2pUCPR0sX7t1hhCXNXKC45p3Na430Orgq+7Bm4aJXsCHITORY3//GUELosNxN+/PYbZr/6Gh2cOwFUjQuGiVrZ/AiICAISEBAM4PwYnvTwdWVVZAIAh/kMQ5x/X4XMpBf7s2RoDjsREUUR9Q+M4kvrzA14tnxtbjC+prDNaP6+oa0BVvbHV87qqlfBuvFwT4KnFJf0DEObjCpVCgLYxkJhFEafyq3BJTABCvF3g6aKCp1YNF7WCYYQwb1goRkX64Knvj+Ox9UexcuNJ/GVMBG6ZEIk+3q6tPqdp7JFWxV/G1LtVG6qhHqCGeq8aybXJ2Jm1E9Fe0bg0/NJOn6usvuyil69IGgw4Evj3ryn46WieNci0NShWo1RA56qGzlVlGVfiooa/pxb9At3h6WIZW+LjpoGXm+VzbzcNfNws4034lzZJIcTLFWsWjUZGcQ0+2ncOn+w/hzW70jBzcBBunRiFCX39rGHYbBbx4BdJ2HGqEEun9cPtk6L5fUi9Um1DLXbn7EawKRhKDyX8Kvy6FGyapJandqq3h7qGAUcC21MK4aJW4tqRfawDYnUuzYMMQwrZkyh/d/xj3mA8PHMAvj2cgw/2ZOCmtfsxMMgTiyZG4pr4PvjnLyn48Wgurhgagv/8ehof7T2H/7t8IK6J78OxU9RrmEUztmdtx5zoOdjpthP1mfWoqa7p1jnrjHVwVbXea0rSYcCRgAABIyK8cN9l/eUuhahT3LUq3Dw+EgnjIrA3rQTv78nAP747hhd+OolagwnPXR2HW8ZH4mxxDf75yyk88tURvLv7LJbN6I9gnUuL80X5u8PLVS1DS4ikd7LkJDKrMjEtfBoUgsK6HlVHZzJuTU1DDVQKvvX2BP5floAgAKI888ARSUIQBEyM8cfEGH9kldbi0wOZCPTU4pbxkQCAaH93vJ0wConnyrDy55O4+6PEVs+jUSkwc3AQrhsZhkv6+0PVg7fyE0nFLJrx89mfMTpoNGL9Yq3bmwJOR+bBaY3BZMCWc1twZb8rJamTLo4BRwKCIEBkwiEnEe7rhsdmD2p136hIH3x1zwSkFVXDYGz+PW8WRexJK8Y3iTm47f2D8PfQ4uoRoUgYH4lof/dWz0dkj/bl7cO08GlwVzf/vvX09AQAVFRUdOm8e3P34oq+V0AhMPj3BAYcCQhgDw71HoIgICbQs9V9cX28sOSSvjieW4lvDmXj28M5+OxAJtbeOhoT+/n3cKVEXWM0G1uEG+B8D05BQUGnz3mu8hx8XHygVvASbk9hjJSAIACNqxcR9XqCICCujxeeunIIfntsGkZF+WLxuoP49Xi+3KURtausvgw+2tZv4W4KOLm5eZ0+b0ZFBoYFDOtWbdQ5DDgSUAgCOrCmI1Gv46ZR4X+LRmNmbBCWfnIIXydmy10S0UWdKDnRbNzNhZoCTl7+xQNOg7kBP6X/hAN5B6zbeFmq5/H/uAR4iYqobRqVAq8vjMcNo8PwyFdH8N7us3KXRNQmk2hq8y6npjE41e0MMt6VtQuXR14OlUKFgpoCGM1G3jklA/4flwAvURFdnFIh4MVrhsLLVYNnfzyB8loDHpo5gDNsk11p72aRph6cizlXeQ5hnmFQK9UYGTQSv+f8jsNFhzE2eKxUZVIHMeBIQIAA5huiixMEAY/PGQRvNzVe2ngKFXUNeOrKIVBw0kCyE6nlqYjxjmlzf3sBR2/S41TpKcyKmmXdNqnPJMnqo85hwJGAIFhukSWi9t1zaT94uaqx4ttkVNQ14F/XD+d8OWQX8mryMCVsSpv7Lww4oii26IE8mH8Qk/tMtll91Dn8rSIBgR04RJ2ycGwE3lw4Ej8ezcOKb5M5jxTJriPfg+7u528d1+v1LfYP9huMsxUcY2Yv2IMjAQECBxkTddLcYSHQG014+MsjCPZyxcMzB8hdEvViR4qOYKj/0Iseo1Cc7xOorq6Gi0vz5UoyKjIwwIffx/aCAUcCCgUvURF1xbUjw5BfWY9XNqUgSKdFwrhIuUuiXqpcX44RLiM6fHxVVRX8/ZtPXllrrIWHpv2ByNQzGHAkIEDgJSqiLlp6aT8UVNTjH98dQ6CnC2YODpK7JOplMioy0MejT6ee09qCmwI4YN6ecAyOBAQBHIRD1EWCIODJK4dg1pBg3P/ZISSeK5O7JOplMqsy0d+nf7vHpaWlWT//+z+ebLGf04XYFwYcifAbm6jrlAoB//3LCAzr4407PziItKKWfx0T2UKDuaHDx+7YscP6+YED+21QDUmJAUcCCkGA2Sx3FUSOzUWtxNpFo+HvocWt7x1AYWW93CVRL3Aw72CHJ+EbMWIEAEDpGYAhgwe32O+h9kCV4eKzHFPPYcCRAGcyJpKGl5saH9w+FkaTiMXrDqKqvuN/XRN1ltFshMFsgIvKpf2DAQwZMgQKpRKmqiIEBgS02O/r4ouyel5itRcMOBLgWlRE0gn1dsX7t49BVlktln58CAYju0fJNrKrstHPu1+Hj3dxcUH/AYMAAAGtBJx6Uz20Sq1k9VH3MOBIQOBq4kSSGhSsw5pbRuPA2VK8+PNJucshJ5VekQ4/F79OPWfMqHgArQccvVHf4d4gsj0GHAlYltJhwiGS0oR+fnhsziC8vycDB86Wyl0OOSGdRge9qeWMxBfTNA7nz3PgAECFoQI6jU6K0kgCDDiS4EzGRLaweGIURkX64LFvjqLOYJK7HHIyo4JGYW/uXpjMHf/eago4rfXgAGixPhXJhwFHAlyLisg2lAoBLy8YhpzyOvxnc4rc5ZCTEQQB0yKmYWvm1g4/Z9y4cfjhhx8xbdq0FvvMIseL2RMGHAkouJo4kc3EBHrg4ZkD8O7usziUyTtUSFquKlf09+mPbZnbUFrf/qVQDw8PzJs3F35+zcfuHC85jiA3zsJtTxhwJMDFNols687J0RjaxwuPfn0U9Q28VEXSivaKxmURl+FQwaEuPf9A3gG4Kl0x0HegxJVRd3Qr4Lz00ksQBAHLli2zbluzZg2mTp0KnU4HQRBQXl7eoXPl5OTg5ptvhp+fH1xdXTF06FD88ccf1v2iKOLJJ59ESEgIXF1dMWPGDJw5c6Y75UuGl6iIbEulVOCV64bjXEkNXt9qHz/35HwG+w3G8eLjnXqOKIqoN9Wjr3dfG1VFXdXlgHPw4EGsXr0aw4YNa7a9trYWs2fPxooVKzp8rrKyMkyaNAlqtRobN27EiRMn8O9//xs+Pj7WY1555RW8/vrrWLVqFfbv3w93d3fMmjUL9fXyz3YqCJZvciKynYHBnnjgsv5YvSsdydkVcpdDTijUIxRl+s5dBj1RcgLRXtE2qoi6o0uriVdXVyMhIQFr167F888/32xfU2/OhWt2tOfll19GeHg41q1bZ90WHX3+G0YURbz66qt44oknMH/+fADAhx9+iKCgIHz33Xe48cYbu9IMyQgCL1ER9YR7pvbDpuP5+NvXR7DhvsnQqHiVnaQjiiIUnfi7P7koGSqFCuGe4TasirqqS78d7r33XsydOxczZsyQpIgNGzZg9OjRuP766xEYGIj4+HisXbvWuv/s2bPIz89v9npeXl4YN24c9u7dK0kN3WFZTJwJh8jW1EoFXrluGFILq/HW9lS5yyEnU1BbgEC3wA4fX6YvQ6xfrA0rou7odMD5/PPPcejQIaxcuVKyItLT0/HOO++gf//++OWXX7B06VI88MAD+OCDDwAA+fn5AICgoOYj1IOCgqz7/kyv16OysrLZw1bYg0PUc4aEeuGvU/vhre2pOJlnu59r6n0yKjMQqYvs0LF1xjpolBobV0Td0amAk5WVhQcffBCffPIJXFykm47abDZj5MiRePHFFxEfH4+77roLS5YswapVq7p8zpUrV8LLy8v6CA+3XRcibxMn6ln3XdYf/QI88Levj6DBxLlHSBpGsxFqpbpDx+7M2okxQWNsXBF1R6cCTmJiIgoLCzFy5EioVCqoVCrs3LkTr7/+OlQqFUymrt2+GRISgsF/Wno+NjYWmZmZAIDg4GAAQEFBQbNjCgoKrPv+bPny5aioqLA+srKyulRbR3CxTaKepVEp8M/rh+FEbiU+P5ApdznkJAR0bBbiPbl7MCZ4DJQKpY0rou7oVMCZPn06kpOTkZSUZH2MHj0aCQkJSEpKglLZtX/sSZMmISWl+Sylp0+fRmSkpaswOjoawcHB2Lr1/GyTlZWV2L9/PyZMmNDqObVaLXQ6XbOHrQiCwBE4RD1sWJg3ZscF48O953gXI0miI2Mpz1Weg4/WB36unVukk3pepwKOp6cn4uLimj3c3d3h5+eHuLg4AJbxMklJSUhNtQwAbApEpaXnZ4icPn063nzzTevXDz30EPbt24cXX3wRqamp+PTTT7FmzRrce++9AGCda+f555/Hhg0bkJycjEWLFiE0NBRXX311d/8fdBvX2iSSx83jInGmsJqLcVKPyazM5MBiByH5PZarVq1CfHw8lixZAgCYMmUK4uPjsWHDBusxaWlpKC4utn49ZswYfPvtt/jss88QFxeH5557Dq+++ioSEhKsxzz66KO4//77cdddd2HMmDGorq7Gpk2bJB0L1FWCIHAMDpEMJvTzQ19/d3yyn5epqGdwvSnHIYi9pG+3srISXl5eqKiokPxy1SNfHcHZ4hp8s3SipOclovb977d0vLzpFPYunw5/D63c5ZAD252zG5P7TG5zf1p5GjQKDcJ1nPemJ3X1/ZuzZEnAMsi4V+REIrtz3agwKAQBX/2RLXcp5MBSSlPanZE4pzqH4caBMOBIQBAAM/MNkSy83TSYNywUnx44BzN/EKmLcqtz0cejj9xlkIQYcCSg4F1URLJKGB+BrNI67DpTJHcp5IDMorndSfvSytO45pSDYcCRgMCJcIhkFR/ujdgQHT7ex8HG1HmiKEIQLj4HTnpFOteccjAMOJJgDw6RnARBwM3jI7DtVAFyy+vkLoccjFKhvOjdUQfyDiDOL64HKyIpMOBIgEs1EMlv/og+cFUr8e3hHLlLIQfU1izGx4uPw9fFFyEeIT1cEXUXA44EBIFXqIjk5qFVYUioF04XVMldCjkYg8nQ6rILBpMBJfUliPGJkaEq6i4GHAkI4GriRPagj48rcsp4iYo651TpKQzyGdRi+8H8gxgXMk6GikgKDDgSEHiJisguhPm4IodjcKiTyvXl8HbxbrZNb9KjwdwArZKTRzoqBhwJKNoZfU9EPaOPtyvyK+thMHI6feqenVk7LzqrMdk/BhyJsAOHSH59fFwhikB+Rb3cpZCD0Jv0zXppahpqsOXcFgz1HwqVQiVjZdRd/NeTgCAAIm8UJ5JdH29XAEB2eS0i/NxkroYcQU1DDTQKNxwrPobiumJ4qD1wadilUCvVcpdG3cQeHAkoBIFLNRDZgVBvV6iVApKzK+QuhRzEM9+dwfXvfg81vPHzfh9k5QUz3DgJBhwJcLFNIvvgolZiTlwIPj2QyXWp6KJEUcR3h3PwfVIxjJXxmPWvI/jyj2y8syNN7tJIIrxEJQHLJSoisgeLJkTiulW52HWmCFMHBspdDtmZcyU1+CYxG98m5SCr9Pwdd5f098e5klpE+PLSprNgwJGAIHAeHCJ7MSrSB7EhOny09xwDDlnllNfhtS2n8XViNtw1KlwxNATXXNcHY6N8oVBY7oSd98Zv8HW/+KKb5DgYcCRgmcmYCYfIHgiCgEUTIrHi22RkldYinH+R93rbUwpx90eJ8NSq8I95g7FwbARc1C1nLh4S4oW96SUwm0Vr6CHHxTE4EhC42CaRXZk/IhQeWhU+3n9O7lJIZnqjCc9sOI4xUT7Y+eg03DYputVwAwALRoUhs7QW+86W9HCVZAsMOBLgWlRE9sVNo8L1o8Lx5cEs1DeY5C6HZCKKIl7fegbZZXV4ct4QeGgvftFiTJQPInzd8HNyXg9VSLbEgCMBriZOZH9umRCJstoG/HiUb1a9UWmNAQ9+noS3tqdh2Yz+GBjs2e5zBEHAuGhf/JFR1gMVkq0x4EiAi20S2Z9of3dMGRCAj/ZmyF0K9TCzWcSdHxzErjNFeO3GEbjvsv4dfu4lAwJwKr8KZ4trbFgh9QQGHAlwKSoi+7RofCSOZFfgSFa53KVQD0rKLsehzHK8fmM85o/o06nnXj44CH7uGry3+6yNqqOewoAjAQG8REVkj6YNCkQfb1d8uJeDjXuTg2dL4a5RYlKMf6ef66JW4taJUfgqMQsl1XobVEc9hQFHApwHh8g+KRUCbh4fiR+O5qK0xiB3OdRD0otq0DfAA8ou3up9y/hICBDw8b5MiSujnsSAIwEutklkv/4yJhwA8OUfWTJXQj3BbBaRmFmGmECPLp/Dx12DucNC8MPRXAkro57GgCMBDjImsl++7hrMGxaCj/edg4nrUzm9H47mIrWwGgnjIrp1nhmxgUgtrEZeRV37B5NdYsCRgCCAq4kT2bErh4Uiu6wOueV8s3JmBqMZ//71NGbEBmJ0lG+3zjU4xAsAkJJfJUVpJAMGHAlYLvMy4RDZK42Kv+p6g19P5COztBb/d/nAbp8rzMcV/h5a7EsvlaAykgN/6iXAQcZE9o0/n73DN4nZiI/wRmyIrtvnUigETBngjx0phRJURnJgwJEIbxMnsl9NNwFwzirnVVhVj11nirFgZJhk55w6MBCn8quQVVor2Tmp5zDgSEAhcLFNInvW9PeHwITjtDYk5UIpCJg3LESyc86IDYS7RomvE7MlOyf1HAYcCXCxTSL71vTjyXjjvH48mofLBgXC200j2TndNCpcOTwUXydmw8w7SRwOA44EBFhWrSUi+9T088kOHOeVWVqLuD7dH3vzZ9ePDkdOeR32pJVIfm6yLQYcCbAHh8i+ne/BYcJxVraa42hkhDf6BbhzokgHxIAjAY7BIbJvQZ4uAIATeRUyV0K2MijYEwczyiQ/ryAIuGF0ODYdz0etwSj5+cl2GHAkwktURPYrNsQTQTotDp0rl7sUspGFYyOw83QRvrJBT0tcHy8YjGaUVHM9M0fCgCMBQRA4kzGRHRMEAa5qJeoaTHKXQjZydXwfzB0Wgr99fRTv7T4r6bkDPbUAgBzOhO1QGHAkoOBim0R2b1iYN/alc6CoM3vk8oHQKBV4edMp1BmkC7NR/u7QqhRIyiqX7Jxkeww4ErDcRSV3FUR0MTMGB+F4biWyyzhpm7OK9nfH5oenQG80Y+upAsnOq1YqMG1gIH46mifZOcn2GHAkIHCQMZHdmzYwAF6uary65YzcpZANRfq5Y1CwJ3amFEl63vkjQpGcU4G0ompJz0u2w4AjActt4ow4RPbM00WNJZdE4+fkPBiMZrnLIRuK9HNDboW042WmDQqEp1aFr/7grMaOggFHAlxsk8gxzBoSjFqDiVPvO7m8inqEebtJek4XtRK3TIjEe7+fRUp+laTnJttgwJGAAPASFZED6B/kiTlxwVj3+1n2ujops1lEbnk9vN3Vkp/7/sv6o6+/O+744CDSeanK7jHgSEAQuJo4kaO4cWwEzhRW40g2J/1zRr+eKEBxtR6XDw6S/NyuGiXWLhqN+gYTXtp4SvLzk7QYcCQggJeoiBzF5Bh/hHi5cOp9J1TfYMLTG47jskGBGBnhY5PXCPd1w1+nxmBHShEqahts8hokDQYcCSi4vA2Rw1AqBFw/KgzfH85BZT3foJxJUZUe+ZX1+MuYcAg2XFl13rAQGM1mbDzG28btGQOOBJp+jnhNn8gxJIyPhN5o5h0xTqaPtysCPLU4dE76NakuFKhzwaQYf7y/JwN6I2fHtlcMOBJoWqGYyzUQOYYgnQuuGBqCD/Zk2GwVaup5CoWASwcEYHtKoc1f6/E5g5BWVM15lewYA44E2IND5HhumxSFzNJabDtl+zdD6jnxEd5IK6qxeXAdEuqFZTMGYNXONC4BYqcYcCTQdK2X8YbIccRH+GBEuDfW/S7twowkr1AvV5jMIgqr6m3+Wvdc2g9jIn1x27qDOFPAuXHsDQOOBJqGsvFWcSLHctukKOxJK8Gp/Eq5SyGJhHi7AAByy20fcJQKAe/fPgZBOi2e/fGEzV+POocBRwLnL1HJWwcRdc4VQ0MQpNPigz0ZcpdCEgnzcYNCAE7m9UxoddOo8NjsQfjtTDGOZpf3yGtSxzDgSEBhw9sRich21EoFFk2IwjeHcpBfYfu/+Mn2PLQqTIrxxyf7M3tsAPnlQ4LRx9sV7zMo2xUGHAmwB4fIcd0yIRJuGiXe2p4qdykkkYdnDsDJvEp8uv9cj7yeUiHg1omR+PFIHoqq9D3ymtQ+BhwJcQwOkePRuahx95R++PxgJrJKa+UuhyQQH+GDhWMjsHLjKVT10GSOfxkdAaVCwGcHMnvk9ah9DDgSUPAuKiKHduvESHi5qvHGNs5p4iwemB6D+gYTfjzaM7MNe7mpcXV8H3y87xwaTOYeeU26OAYcCXAeHCLH5qZR4a9TY/DNoRyuEu0kQrxcERuis/msxhdaPDEKhVV6bDyW32OvSW1jwJEAZzImcnw3jYtAgIcWr21lL44zqG8w4VxJLaL83XvsNQcGe2JiPz+8z7mV7AIDjgSsN1Ex4BA5LBe1EvddFoMNR3KRks9J2xzd1pOFqNYbccXQkB593VsnRuFQZjlvGbcDDDgSaFpNXGTCIXJoN4wORx9vV7zw80mOo3Bw3x7OwfBwb0T3YA8OAMyIDeIt43aCAUcSjYOMmW+IHJpGpcCKK2Kx63QR3tzG28YdVVmNATtPF+KaEaE9/tq8Zdx+MOBIoOkSFW8TJ3J8VwwNwaQYPxzMKJW7FOqin5LzYBaBecN7PuAAllvGAeD7pBxZXp8sGHAkwCE4RM7l+lHh2JNW0mPT/ZO0vjucg0v6+8PfQyvL63u5qTF1YAB+6KFb1Kl1DDgSsM6Dw4RD5BTmDQtBH29XvL0jTe5SqJOySmvxx7kyXBPfR9Y6rhweiiNZ5cgs4eSRcmHAkQDnwSFyLiqlAn+d1g8/Hs3F8dwKucuhTvj1RAFc1ArMHBwkax3TYwPhqlbix+RcWevozRhwJGANOPKWQUQSumF0OPoFeODv3x7rsUUbqfvyyusQ6u0KN41K1jrcNCpMjw3ED0d4mUouDDgSEHiJisjpqJUKrLx2KJKyyvFJDy3aSN1XWmOAr5tG7jIAWC5TncyrxNniGrlL6ZUYcCRwfpAxEw6RMxkT5YubxkXglU0pyC2vk7sc6oDK+ga4a+XtvWkyOcYfaqWA384UyV1Kr8SAI4GmHhz2YhM5n8fnDIK7VokV3yZznJ0D6BfogVP59nH3m7tWhfgIH+w+Uyx3Kb0SA44ErD04/OVH5HR0Lmq8eM1Q7EgpwreHOa+JvRscokNBpd5uQs7kGH/sTS+BkTNj97huBZyXXnoJgiBg2bJl1m1r1qzB1KlTodPpIAgCysvL2z3P008/DUEQmj0GDRrU7JipU6e2OOaee+7pTvmS4W3iRM5temwQ5o8IxTM/nEBhVb3c5dBFDAjyBAAUVxlkrsRiUow/quqNOJrDu/F6WpcDzsGDB7F69WoMGzas2fba2lrMnj0bK1as6NT5hgwZgry8POtj9+7dLY5ZsmRJs2NeeeWVrpYvqfO3ictbBxHZzlNXDoFKIeCp74/LXQpdxGcHMuHvocHoKB+5SwEADA/zgqdWhd95marHdSngVFdXIyEhAWvXroWPT/NvomXLluHxxx/H+PHjO3VOlUqF4OBg68Pf37/FMW5ubs2O0el0XSlfchxkTOT8fN01eGb+EGw8lo+fk3nrr73afKIA18T3gYtaKXcpACxzKo3v54fdqQw4Pa1LAefee+/F3LlzMWPGDMkKOXPmDEJDQ9G3b18kJCQgMzOzxTGffPIJ/P39ERcXh+XLl6O2tu0ZIvV6PSorK5s9bIW3iRP1DnOHhmDWkCAs+yIJq3ZylmN7ozeakF9Zj5hAD7lLaWZyjD8OZZahqr5B7lJ6lU4HnM8//xyHDh3CypUrJSti3LhxeP/997Fp0ya88847OHv2LC655BJUVVVZj7npppvw8ccfY/v27Vi+fDk++ugj3HzzzW2ec+XKlfDy8rI+wsPDJav3zzjRH1HvIAgCXrxmKAxGM97iauN2p7BSD1EEgr1c5S6lmemxgWgwidh2qlDuUnqVTk0WkJWVhQcffBCbN2+Gi4uLZEXMmTPH+vmwYcMwbtw4REZG4ssvv8Qdd9wBALjrrrusxwwdOhQhISGYPn060tLS0K9fvxbnXL58OR5++GHr15WVlTYLOU2XqLiaOJHz8/PQYvUto3D3R4nYfKJA9iUB6Dy90XKnkpvGPi5PNQnzccPwcG/8nJyH+SPkXSOrN+lUD05iYiIKCwsxcuRIqFQqqFQq7Ny5E6+//jpUKhVMJpMkRXl7e2PAgAFITW37L6Rx48YBQJvHaLVa6HS6Zg9b4SUqot5lZmwQZg4Owr2fHMJnB1peTid5uKgtb2k1eqPMlbR0RVwwdqQU2WVtzqpTAWf69OlITk5GUlKS9TF69GgkJCQgKSkJSqU0qbm6uhppaWkICQlp85ikpCQAuOgxPUXR1IXDi1REvYJCIeDNm+Jx1YhQPLXhOPIreOu4PcgstYzL9PfQylxJS1cMDYHeaLaLy1SphdWoqHP+8UCdCjienp6Ii4tr9nB3d4efnx/i4uIAAPn5+UhKSrL2rDQFotLSUut5pk+fjjfffNP69SOPPIKdO3ciIyMDe/bswTXXXAOlUomFCxcCANLS0vDcc88hMTERGRkZ2LBhAxYtWoQpU6a0uE1dDk1jcDiTMVHvoVUp8dSVg+GmUeLN7WfkLocAfLzvHCL93BAbYh932F4o3NcNQ/t4YeMx+e/Am/GfnRj+zK/4z+bTTh3OJV+wY9WqVXjmmWesX0+ZMgUAsG7dOixevBiAJbAUF5+/ZS47OxsLFy5ESUkJAgICMHnyZOzbtw8BAQEAAI1Ggy1btuDVV19FTU0NwsPDsWDBAjzxxBNSl99FvERF1Bt5uqhxz6X98O9fU3D3lH4I93WTu6ReK62oGhuP5eOFq4dCeb5b3a5cMTQEr209jeJqvU17meoMJmw7VYhvDmVjf3oJfD008HbVYHZcMKbHBlqPe/e3dLy1PRV/GROOp68cAo3KuRY3EMResr5AZWUlvLy8UFFRIfl4nEOZZbj27T3YtOwSDAq2v78ciMh2ag1GTHllB2JDPLF20Wi7mX+lNxFFEXd+8AeO5VZg16PToFXZ579BaY0Bl/17B6YPCsK/bxhuk9c4lFmGuz9KRFGVHoOCPTEjNghGs4jUwmpsOVkAQbD8MX7ftBjcfWlffHEwCy9vOoXxff3w/NVxCPFytbug09X3b/tYctXBcakGot7LTaPCK9cNxdKPD+H+zw7j7YSRUCvt6w3C2X17OAdbTxVi7aLRdhtuAMtkkY/PHoTH1yfj+tFhGN/XT9LzV9Q24P++PIJQLxd8efcERPu7N9tfrTfigz0Z2JdeglFRPvB0UePOS/picIgOd32UiEv/uQNermq8vGAYBgV7IsLXDQo77Q3rCPbgSOBIVjnmv/U7fnpgMoaEekl6biJyDNtOFeCuDxPx16n98PDlA+Uup9eobzBh3ItbMW1gAF69MV7uctplNou4btUeVNYb8fMDl0jSW2I2i/g6MRsvbzoFg9GMb++d1OnJDgsq63G6oArv/56BrY0DoYeE6jAywgfuWhUenN4fCgWw+0wx/Dy0GB7mZb2D2NbYgyMjrkVFRJcNCsI9l/bD6l3puGFMOMJ8OB6nJ5wuqEJFXQMWTYySu5QOUSgEvHDNUMx7Yzf+tzsdf50a063zHckqx5MbjuNIVjmuie+D5XMGIVDX+XnqgnQuCNK5ID7CB0s/TsTAIE+kF9dgb3oJzpXUYHdqEWr0JpwtrgEAXDk8FP+6fphd95gx4EhA0UMplojs29Kp/fDlH1l48vvjWHPLKKh4qcrmghvfzAsr9TJX0nGxITrcPikKr289gyuHhXZpcHpJtR7//CUFX/yRhUHBOnx59wSMjfbtdm0eWhU+umNcs207UgqxYn0yhoZ54bUbR+BscQ3+9vVRAMAbC+2314w/fRLiTMZEvZu7VoWXFgzFztNFeHULbx3vCQGeWrioFcgqbXttQnu0bMYA+LhpsOLbZJg6OcfI/vQSTPvXDvycnIdnrxqCH+6bJEm4acvUgYHYs3w6Vt8yGsPCvDF/RB+svGYofjiSi+TsCpu9bncx4EiAl6iIqMllg4Lw0Iz+eGtHKvakcQVpW0strEZ9gxlRfxpQa+8sYXgYfk8t7tTCrSdyK/GXNfsQ5uOG7Y9MxS0TomTpKbw6vg/CfV2x9rf0Hn/tjmLAkYDQNA+OzHUQkX1YOjUG46P9sOzzJGQ0jlkg2/jxaB48tSpMGeAvdymddumAANw6MQqrdqahuFoPczs9OelF1bji9d8AAK8vHAE/GWdsVioEXBsfht2p9hviOQZHAorGmNhLbkgjB5GTk4PHHnsMGzduRG1tLWJiYrBu3TqMHj0aALB+/XqsWrUKiYmJKC0txeHDhzFixAh5i3YSSoWA124cgRvX7sO17+zBe4vHYES4t9xlOR1RFPFTch5mDgmy68GuF3PPpf3wyf5MjH5+CxQC4O2mgY+bGr7uGni5qlGtN6KwSo/0ohq4Ns6xdOXwUMQEespcORAT6IHSGgNKawzwddfIXU4LDDgSaOrB4VINZC/KysowadIkTJs2DRs3bkRAQADOnDkDHx8f6zE1NTWYPHkybrjhBixZskTGap1ToM4F39wzEYveO4DrV+3B5ocudbjLKPYupaAKqYXVWHHFILlL6bIgnQu+v3cSTuVXos5gRlmtJTCU1RhQVmuAv4cWvu4apBfV4IbRYVg6NQaBnvax1lZ8hDcA4MDZUsyOC5a3mFYw4EhA4GKbZGdefvllhIeHY926ddZt0dHRzY655ZZbAAAZGRk9WVqv4uOuwQvXxOGqN39HbkUdA47EfjqaBy9XNSbHBMhdSrfEhujscv2s9oT5uGFAkAe+O5xjlwGHY3Ak0JRveIWK7MWGDRswevRoXH/99QgMDER8fDzWrl0rd1m9kperWu4SnJIoivjpaB5mDQmyu6UFepNbJ0bh1xP5OJJVLncpLfC7QgLWu6jkLYPIKj09He+88w769++PX375BUuXLsUDDzyADz74QO7Sep2mtalO5FbKXIlz2XqyEOnFNbg6vo/cpfRqN4wOx5BQLzz0ZRLqDCa5y2mGAUcSnOiP7IvZbMbIkSPx4osvIj4+HnfddReWLFmCVatWyV1arxOkc8GiCZF4edMpHMosk7scp2A0mfHSplOYFOOHCRKv50Sdo1Yq8N+/DEdOWR3e3pEqdznNMOBIiJeoyF6EhIRg8ODBzbbFxsYiMzNTpop6tyfmDsawMG8s/diyyjN1z5d/ZCO1sBrL58T22HpI1LaYQE9cOTwUW08Wyl1KMww4Ejg/0R8TDtmHSZMmISUlpdm206dPIzIyUqaKejeNSoG3E0bCLAL3fXoIRpNZ7pIcVo3eiP9uOY2rR4Qirg8XN7YXg4I9kV5cLXcZzTDgSMA6yFjWKojOe+ihh7Bv3z68+OKLSE1Nxaeffoo1a9bg3nvvtR5TWlqKpKQknDhxAgCQkpKCpKQk5Ofny1W2UwvSueCtm0Yi8VwZXtp4Su5yHNZnBzJRUduA/+OK7Xalst4InYt9DahnwJFAUxcpO3DIXowZMwbffvstPvvsM8TFxeG5557Dq6++ioSEBOsxGzZsQHx8PObOnQsAuPHGGxEfH89xOjY0NtoXK66Ixf92n8UPR3LlLsch/ZSch8sGBXZpgUqyndTCKvQNsK9pEDgPjgR4BZjs0bx58zBv3rw29y9evBiLFy/uuYIIAHDbpCgcyS7HY98cRb8ADwwOdbz5T+SUW16HKf0de94bZ3QitxKXDQqSu4xm2IMjIZEXqYioHYIgYOW1Q9E3wB23vLsfpwuq5C7JYZTVGFBcbUCwl4vcpdCflFQbEKSzjxmWmzDgSEDgIBwi6gQ3jQof3T4OgToX3Py//civqJe7JIewamcatCoFZg62r54CAvw8NCiutq87BBlwJMDVxImos3zcNfjg9jFQKQTc+eFB1BqMcpdk13LK67BuTwaWXNIX/jKuok2ti/BzR0ZJrdxlNMOAIwFOw0BEXRHo6YL/3ToG6UU1ePiLIzBzxd42/efX09C5qLBkSl+5S6FWRPu54WxxjdxlNMOAIyHeRUVEnTU4VIfXb4zHLyfy8e/NKe0/oRc6mVeJ9Yez8cD0/vDQ8t4YexTt747MklqY7CikM+BIiIOMiagrZgwOwt9mDcTbO9KQeI7LOfzZK5tOIdLXDQvHRshdCrUh0t8dBpMZ+ZX2M56MAUcC52cylrcOInJcd0/ph+Fh3nj06yOorG+Quxy7sTetBNtTivC3WYOgVvIty175uWsAWO50sxf8bpGAdaI/mesgIselVAj41/XDUFilx5IP/kB9g32tzCwHs1nEiz+fxPBwb1wxNFjucugivF0tAaeyzn7COQOOBDjGmIikEBPoifcWj8GR7HI88NlhuxrPIIfvknKQnFOBJ+ZyUU175+NuWaahyI5uFWfAkQAX2yQiqYyJ8sXbCSOx5WQBXvml965ZVWcw4Z+/pGBOXDDGRPnKXQ61w9NFDU+tCjnldXKXYsWAIyHGGyKSwmWDgvD3uYOxemc6vknMlrscWbzyyymUVBvw+JxBcpdCHVCjN6JKb0SQp/3MMs377STQNNEfEw4RSeX2SVE4nV+F5euTEeXvhlGRvacXY+vJAqz7PQNPXTkYkX72tYAjte5UvmXJkUEhnjJXch57cCRgvUTFhENEEhEEAc9dHYcREd5Y9O4BJGWVy11Sj8gtr8MjXx3BjNhALJ4YJXc51EEn8iqhUgiICfSQuxQrBhwJcOgbEdmCRqXAusVjMCDYE/d8lIjCKvuZY8QWzhbX4IbVe+GmUeGV64ZzYLEDOZFbiZhAD2hVSrlLsWLAkQLnwSEiG3HXqrDq5lEwiSJuf/+g086RU1hVjxtW74VWpcCX90yAb+O8KuQYTuRVYnCoTu4ymmHAkRADDhHZQpDOBR/cNhbnSmpxpxPOkWMyi3jwsyQAwGd3jUcfb1d5C6JOMZrMOJVXicEhDDhOh6uJE5GtDQ7V4f3bxiA5uwJ/+/qoU4Wctb+lY//ZErx24wgE2tFdONQxGSU10BvN7MFxRpwHh4h6wqhIX6y8dih+PJqLyS9vw4ncSrlL6rac8jq8tuUMbpsUjYn9/OUuh7pgb3oplAoBQ0K95C6lGQYcCXAYHBH1lKvj+2D7/02Fj5sGD35+GJkltXKX1GWiKOLZH45D56rCshn95S6HuujX4/kY39cXXq5quUtphgFHAlyLioh6UpS/O95OGAmDyYzL/r0DUY//hLs+/AMVdrQOUEe8sS0VvxwvwJPzhsDTxb7eHKljymsN2JtWgllD7G+tMAYcCfEKFRH1lP5Bnvjh/sm4fnQ4AODXEwW48o3dqNEbZa6sY97dfRb/2Xwaj1w+AHOHhchdDnXRR3vPQaEQMCfO/v4NGXAkcP4SFRMOEfUcnYsaK68dioyX5uLnBy5BYVU9pv1rB17dchqFlfY7Z87nBzLx3I8ncM+l/XDvtBi5y6EuWn8oG29uT8XN4yIR4KmVu5wWGHAkIHAeHCKS2eBQHX68fzJmDA7C6p3pmPjSNtz36SEcOFtqNzdA1BqMePL7Y3h8fTIWTYjEY7MHcjI/B3U8twIPf3kEc4eF4NHZA+Uup1Vci0oCAocZE5EdiAn0xIvXDMVjswfhm8RsfLTvHG5YvRexITr887phiOsjz10uoijilV9S8NmBTNQ3mPDMVUNwy/hIhhsH9vG+TIR6ueCVBcOgUtpnX4l9VuVouNYmEdkRL1c1bp8cja0PX4oPbx8LpQJY8M4e/POXUz0+f87hzDIs/fgQ3tmRhhAvV/z8wCW4dWIUFAqGG0cliiK2nizA3GEhdhtuAPbgSMpOeoGJiAAACoWAKQMCMCbKF29tT8Wa39LxfVIuZsQGYeHYCAwMtt3Kz2U1Bvzj+2P48Wge+ga4Y8UVg3D7pGi7fkOkjsmrqEdhlR6jo+x7hXsGHAlwNXEismeuGiUemTUQc4YGY+7ru/H+ngxsOVmAH++fDC9XteSXirJKa/HSplPYmVKEVxYMw4JRYVCyx8ZpHGlc2T4+3FvWOtrDgCOBph9b9uAQkT0bEuqF5Kcvx9++OopNx/Mx4tnNcNco4eehxYS+fnjx2qFdCiINJjO+PZSDgxmlKK7WY3tKETRKBf42ayBuGBNug5aQnJJzKhCk0yJQZ9/LajDgSIAD5YjIUXi6qPHmTfE4nluJnPI65JTV4aVNp/DFH1l49uohUCqUHTpPncGEn5LzUF3fgF+OF2D/2RJE+bmjpMaA56+Ow/wRoZy8z0n5umtQXtuABpMZaju+5MiAIwFrD46sVRARdYxKqcDwcG8Mb7zEkFFSg28P57R7R2iN3ohDmWX46WgevkrMhlkUoVYqoHNR47Ml4zGur18PVE9yG9rHC3qjGelFNTYdx9VdDDgSspe5JoiIOuPGMRH4ZH8mBjyxEatvGYXLBgWioq4BhZV65JTX4cDZEhw4W4pjuZUwmUX4uWtw9Yg+uO+yGET5uUEUwbuiepFBwZZVw0/lVzLgODteoSIiRxbXR4cbRofhyz+ycfdHiS32h3i5YFy0L/4yJgJjo33RL8C92aV5/g7sXbzc1AjWueB0QZXcpVwUA44EONEfETkyQRDwynXDce+0GBzKLMMLP52Ei1qJRy4fiNFRPujj7cqxhk6gRm9EjcGIAA9tt/89fd01qKq373XPGHAkwKUaiMgZRPq5I9LPHdfEh8ldCtnAX9bsxbGcStw1pS9WXBHbrXM5wtud/Q5/dkCcB4eIiOxRjd6IYzmVAIA1u9K7fT6zWYTCznv12IMjIfbgEBGRvcivqMeWkwXYeboIm08UNNsX9fhPOPXcbLioOzYtwJ9V643wdLHvCGHf1TkIXqIiIiJ7YjKLGL9y60WPGfvCFuxZPh0e2s5Hgcr6Bgac3oCDjImIyJ4k51RcdP+cuGBsPJaPjcl5uH5089mmRVFEUZUeR7MroFQKMJpEeLmqUVpjQHZZLfRGM6r1Rnho7XsiRwYcCQhcTZyIiOxEQWU9HvjssPXrsVG+OJBRip8emIwoP3e4aZQQBAFRj/+EX44XoKhajzqDCe/tPouhYV5Iya9CWW1Dm+f3c9cgzMcVcX10PdGcLmPAkcD5tagYcYiISF5vbktFZX0DZsQGItrfHSuuiG31tvDLBgXieG4F/jhXivLGQHMqvwq3TYzGwGBP3PNx8zmRfN01mBkbhJevG9Yj7eguBhwJMd4QEZGcDpwtxXeHc7BwXES7t4K/t3iM9fOSaj1GPb8FoV6ueHBG/1aPL60xYHdqMURRdIh5kRhwJCDwGhUREcnsWE4Fbn53P0ZGeOPeqTGdeq6fhxYAcCKv0rrtsyXjUVhVj/kj+uCjvRn4x/fHkVNeh3MltYjyd5e0dlvgPDgSsP8cS0REzsxsFvHIV0fQL8ADH9w+Fl5u3R8APKGfH+aP6AMAWDDq/OSPu1OLu33unsCAI4HzHTjswiEiop6372wJTuVX4cl5g6FVdW1uG5/GUFRraLkEg5tGhQmNq8XvSXOMgMNLVBJoukTFMcZERL1bVmkt9qWXIKOkBm9tTwMAbFp2iXUF7j8ySvHpgUwYTSJUSgGzhgRj1pDgbr/u5wey0DfAHeP7+nb5HNfEh+G9388i8VwZLukf0GK/i9rSJ/J7aglMZhFKO19BngFHQsw3RES9V2FlPea9sRsVdc1vsZ796m849I+ZaDCZsXjdQXhoVYjyd0NlnRHrD+XgvmkxWDwpCqcLqvD8jydxuqAKggBcNyocz18d126QKKsxYNOxfDwya0C3Bv9eOTwE7/1+Ft8ezmk14JTXNSAm0AOphdVIPFeGsdFdD1M9gQFHQuzBISLqnc4W12Dav3YAAPYtnw5fdw0Wrt2HxHNlAIDiaj2e+eE4XNQK/LJsCrzc1BBFEW9uS8V/t5zGm9tTAQBjo33x5JWDUVJtwBvbzsBgNONf1w+7aHD59nAORIhYMLJ7i6TGhuis5/vPDSNa7K+oa8C0gYGorGvApmP5DDi9hQPcMUdERDawJ60Yi9cdhCBYZggO0mkhCAK+WToRpwuqcO3be3D5f3fBXaPE2kWjrQOABUHA/dP746ZxEdidWgxBEHBFXDBUSsuloCh/Nzz0xRFMGxSAecNCW33t0hoD/vdbOi4fEmy9E6qrmtalauuP9YraBvi4qTFrSDB+OZ6Pf8xrfX4de8GAIxEBHGRMRNTbnMqvxN0fJmJslC9W3zIK7n9a12lAkCd+fuAS/HoiH5cOCED/IM8W5/Dz0FrvVrrQNfFhWH8oB+t+z2g14JjMIh747DD0RjP+3s6cN5315zE2oiiioq4BXm4axIbo8NG+c3Z/uzjvopKIIAi8REVE1IvklNfh1vcOIMLPDataCTdNIvzccOclfVsNN+25cngoDmWWoahK32Lfv35NwZ60YryxMB6h3q6dPndrBgVbajx5wXw4AFBjMMFotqxJNTbaF0qFYPe3izPgSIj5hoiod6iobcDi9w5ArVRg3W1jurQid0dMHxQIAcDWkwWoaBz7snLjSazdlY53dqThsdmDMDHGX5LX+s/m0ziVXwUAuPODP5BaWGXdV9wYsHzdNPB0UWN4mJfd3y7erYDz0ksvQRAELFu2zLptzZo1mDp1KnQ6HQRBQHl5ebvnefrppyEIQrPHoEGDmh1TX1+Pe++9F35+fvDw8MCCBQtQUFDQnfIlJQAcZUxE1Es8ueEYiqr1+OD2sQj0dLHZ6/h5aDGhnx+e/+kk4p/9Ffd8nIjVO9Pxws8nER/hjbum9JXkdX44kovXt57BA9P747dHp0GjUuD1ranW/cdzLT06g0IsPTyTYvyxN60EZrP9vu91OeAcPHgQq1evxrBhzRfdqq2txezZs7FixYpOnW/IkCHIy8uzPnbv3t1s/0MPPYQffvgBX331FXbu3Inc3Fxce+21XS1fcnY8zqpdZrOIOoNJ7jKIiGxGFEXsSS3G90k53f59l5JfhQ1HcvG3WQPRL8BDogrb9vDMgbh8SBBevGYofnt0GvavmI6rhodi2Yzu3RbexGQW8c9fUjBzcBAemtEf4b5uiA3xRH5lvfWY7LJaaFUK+LhpAFgCTlltQ7OlHexNl/rUqqurkZCQgLVr1+L5559vtq+pN2fHjh2dK0SlQnBw65MdVVRU4N1338Wnn36Kyy67DACwbt06xMbGYt++fRg/fnyn2yA1AYKkl6jMZhEGk9nyMJpRZzChvsGEugYT6gyWj+e/Np//uvE4vdGM+gYT6ps+XrCtzmBCbeM5ag1G1DeYAQBPzhuM2ydHS9gKIiL5pRVV4x/fHcOetBIAgLebGosmRGHxxCj4ums6fb6fk/Pg5arGDaPDpS61VaMifTAq0qfZttcXxkt2/p+T85BZWos3b4qHIAioM5iwL70UiyZEWo8ZGekDvdGMvWklmNzfHyXVBgBAVX3LWY/tRZcCzr333ou5c+dixowZLQJOV505cwahoaFwcXHBhAkTsHLlSkRERAAAEhMT0dDQgBkzZliPHzRoECIiIrB37167CDgQgF+PFyCjuBYNJjMaGoNJU0BpaAwrDUaxlW0XHmfZb+pEt58gAK5qJVzUSriqldCqFdCqlHBRK+DS+NFDq4K/h+VzV7UKbholXDWW4900Svx78+lmaZ2IyBmIooiHv0hCaa0B/1s0Gv2DPPC/387ire2peHt7KmbEBuGf1w+Dp0vH1246kl2O+HBvqJWOP4zVbBbxxrYzmDIgAMPCvAEAvxzPR0VdA667YP2p0ZE+8HFT40BGKSb398eHezMwsZ9ft2ZOtrVOB5zPP/8chw4dwsGDByUrYty4cXj//fcxcOBA5OXl4ZlnnsEll1yCY8eOwdPTE/n5+dBoNPD29m72vKCgIOTn57d6Tr1eD73+/KjzykrbdqNN6ueH9OIaFFTWQ61UQK1SQKtUQK0SoFEqoFYq4KZVWbYpFdCoFI3HCdZtapXCcuwFz1UrLduawsifP7qoldCqFN3upvzf7rOdClVERPbMbBaRWlSNjcn5OJJdgfdvG4OpAwMBAM9dHYdlM/rjhyO5ePqHE5gY44dFE6I6dF5RFHEkqxy3TuzY8fYsv6Ier209jdMF1Vh57VDr9sOZZegb4I5Iv/O3gAuCgNFRvtiTWoyHZw6AySwizMfNeebBycrKwoMPPojNmzfDxUW6QVVz5syxfj5s2DCMGzcOkZGR+PLLL3HHHXd06ZwrV67EM888I1WJ7Vp329geey1bUAiAmYOkicgJ1DeYcOcHfzROngfcNaWvNdw08fPQYvGkaKzamY4nvz8OtVKBhWMj2j13ZmktymobMDzc20bV94xqvRE3rtmLiroGPDp7IEZFnu+JOZlXhSGhXi2eM39EKO779DC2pxTCw0WFan1Di2PsSaf61xITE1FYWIiRI0dCpVJBpVJh586deP3116FSqWAySTNQ1dvbGwMGDEBqqmUEd3BwMAwGQ4s7sgoKCtoct7N8+XJUVFRYH1lZWZLU5qwUnMeHiJzESxtP4UBGKVbdPBJ//H0GVlxkEjxPF8vf+cvXJyOrtLbdcydllQMARjReznFUH+zJQG55Pb796yT8dWpMs31ZZbWI9HVr8Zwr4kIwdWAAHvnyCEQRqNHb980pnQo406dPR3JyMpKSkqyP0aNHIyEhAUlJSVAqu7ZE+59VV1cjLS0NISEhAIBRo0ZBrVZj69at1mNSUlKQmZmJCRMmtHoOrVYLnU7X7EFtEwSBPThE5PC2pxTi/T0ZeGJuLGbHhbS7fEFTwAGA/2453e75j2RVINLPDT5dGJxsL0RRxGcHMnF1fGiLmYj1RhPyK+sR7tty4kCFQsDLC4ahqt6InaeLUFVv3z04nbpE5enpibi4uGbb3N3d4efnZ92en5+P/Px8a+9LcnIyPD09ERERAV9fSxfY9OnTcc011+C+++4DADzyyCO48sorERkZidzcXDz11FNQKpVYuHAhAMDLywt33HEHHn74Yfj6+kKn0+H+++/HhAkT7GOAsRPgJSoicnRGkxnP/XgCk2L8cMv4yPafADQbXHw4sxyFVfUXndcmKasMwx289ybxXBmyy+pwbSuLc+aV10MUgXCflj04ABCkc8H1o8Pwyf5MpBRUQRRFux2HI/kQ8FWrViE+Ph5LliwBAEyZMgXx8fHYsGGD9Zi0tDQUF5+fATE7OxsLFy7EwIEDccMNN8DPzw/79u1DQMD55dr/+9//Yt68eViwYAGmTJmC4OBgrF+/Xuryey2lQgDHGBORI1t/OAdni2uwfE7HF4H0uKAH52xxDca+sBWJ50pbPbawsh6HMssxLKzl+BRHsutMMXzdNRgb1fIOqKwyy2W6sDYCDgDcPaUfAKC+wYyc8jrbFCmBbs8t/ef5bp5++mk8/fTTF31ORkZGs68///zzdl/HxcUFb731Ft56661OVkgdYVlLiwmHiBzXJ/vOYeqAAMT16XgA0apa/p3/7A8n8P19k1tsP5BhCT5zhoZ0vUg7cKagCoOCPaFQtAyBWaV1UAhAiHfbvVgRfufDT2ZJ7UXDkJwc/yZ+koRCAMxmuasgIuqaYzkVOJJd0aE7oS60oJXLNEeyK3C2uKbF9hO5lQjWuaCPRAtbyiWvoh5hPq23IbusFiFeru3O8TMgyDKDc7Yd9+Aw4BAAy11UJvbgEJED+uV4Pu7/7DAifN1w2aDA9p9wgUPnylrdviEpt8W2t3ekOcWEqKU1Bvi6tz74Oqusrs3wc6H+gZY1qbLLGHDIznGQMRE5olU703D3R4kI9NTiw9vHQtXJ2YVdNZa7f/0uuCtKpRDwfVJOq5fto/zs83JMZ5RU6+Hv0fpdYHnldR3qoQprvMsqhwGH7J3AeXCIyMEczS7Hy5tO4b5pMfji7gktbnnuCA+tZShqRV0DYgItl10mNM5Mv/N0UbNjI3zdHH78TZ3BhBqDCX5tBJyKugZ4u7V/C7y7xvL/Lbus/bmD5MKAQwDYg0NEjsVsFvHk98cxMMgTy2b07/J5mu6iMppFa69GlJ87RoR7441tqdZenKIqPTJLa1sdlOxIDCbLYMu2ft1X1DU0mxuoLXqjZZI/e76LyrH/pUgyvE2ciBzF/vQSLHrvAI5kl+OZq4Z0+rLUhZp6cABA1zgnTn5lPR6YHoPEc2XYl265c2rbqQIA58eeOCovVzX6BrjjaHZFi32J50pRWKVHbEj7E+OW1Vom+curqLfbdQwZcAiAZZAxe3CIyJ5V1TdgyYd/4C9r9iG/sh7vLR6DcX39unXOC3srdK6WgFNQWY9pAwMxOESHVTvTAAACLLdUz4lrfXkgR9I/0AOphdXNttU3mPDCTycxIMgDlw8Oavcc5bUGAIDJLKKuwT6XbOj2PDjkHDgPDhHZs4LKeixedxDZZbV486Z4zB0aIskMuu6t9OCcLaqBIAi4bVIU/vb1UZwrqUGNwQhlK/PGOKJofw/8ePT8XWKiKOL+zw7jcFY51t4yutX5cf6stMYAD60K1XojavTGZj1h9oI9OASA8+AQkX0yGM3YdqoA1769B+W1Bnx9z0TMGxYq2fIAF74xN/XmVOmNaDCZceXwUHi7qbFmVzre35OBS/r7d+jN394F6bQoqtJb/6j9ZH8mNp8owNs3jcSMDvTeAEBZTQNiQyyX6/Iq7PPWefuLXCQLzoNDRPbkRG4l/vdbOjafLEBVvRHDw7yw6pZRCPGSdpI9T+35taiaLlEBQG55HSL93HHD6HCs2ZUOAFi3eIykry2XAE8t9EYzymob8MORXDz74wksmhDZqTvEymoNGNfXFwczylBop3MDMeAQAEsPDi9REVFPMJlFKATLpfHc8jrsPF2ESwcEILRx/pVP92fiie+SEertijsmR+PywcGIDfG0yaKO7lrl+c815z/PLK1FpJ87EsZFWANOe7P7OoqBQZael/Ert8JgNOPm8RF4ct7gTp2joq7BOl9Otd4oeY1SYMAhAJZfNPY6Ep6IOk4URRw4W4qUgipcMTQE/h6tz1jbGWU1BlTVG5FVVovjuRUI9HTBwGBPDAw6v57R76nFOJxZhuScCpTXNuClBcMQfcG8NGaziHV7MvD+nrPILqtDTIAHhoV54+fkPNQ1mKBzUeGV64Zh/9lSrPs9A4smROLJeYO7dYdUR6iUCriqlahrMEFvNMPfQ4viaj3yyi29EpF+7hgV6YPEc2XNengcWf8gT6y+ZRSSssoxd2hIp9buAizfYwaTGZ4uamiUClTVM+CQHVMKAhpEDsIhcmTpRdV4fH0yDpy13Nr81vZULJoQBcASMEZH+SI+whsuauVFzmJRozfi68RsfLo/EykFVdbtLmoF6hssvyuCdS6I8HVDvdGEo9kV8HXXoK+/O3LL67Dovf348u4JCPFyRUVdA5avP4qNx/Jx3cgwjLjUG3vTSnC6oAqLJkbi1glRWPZFEu75+BBc1Uo8MTcWd0yOtkmPTWvctSrUNZhQrTciys8NxdX6ZksyfHTHWJTXNsDLSQIOAMwaEoxZQ7p2R5jRLEIUAbVSgEIBNJjs872DAYcAAAoFOA8OkQPbdqoASz8+hGAvF6xbPAYxgR5Y8W0y3tyWCq1aAbNZxL83n4ZWpcCljStuzxwchEHBzS/9lNYYsP5QNlbtTENZbQMuHxyEey+LgZ+7BoGeWvQL8ECV3ojjuRXYcqIQ5XWW24XvmByNq4ZbBv9ml9ViwTt7cMnL2+HhokJ1vRFqpQKrbh5lfVNNGBfZrP5P7hyHxHNlGBjkCR/39mfSlZKniwrF1XrU6I0I8XYFzpU1GzjrplHBTcO3yyYqhQAXtQIbjuSivsGMwR2YN0cO/BcjALxNnMiR5ZTX4f5PD+OS/gF4Y2G8dX2lj+4YZz3GbBZxMr8SO08X4aejediXXoL/bD6NSD83LJvRH24aFb47nIMtJy0T2l01vA8emtkfYT4t117yclVjYj9/TOzn32o9YT5u2PTgFPx8LA+VdUZ4aJWYNSQYgTqXNtugViowvptz2nRV051UtQYTIhrXWCqw04Gz9kAQBOhc1PjtTDGG9vHChH7y/Lu1hwGHAHCiPyJH9ua2M3DVKPHfvwy3hps/UygEDAn1wpBQL/x1agwMRjP2pBXjo73n8NAXRwAAg4I98ficWFw9IhR+3Ry74+OuadFLY6+aAk613ogBjQNwM4pr5CzJ7kX7u6OwSo+V1w7tsUuJncWAQwA4Dw6Ro8otr8PXidl45PKB8HTp+BgRjUqBqQMDMXVgIM4W10ClEBDm42q3b1a21LQeVa3BiPDGHpx0BpyLen1hPPall2BIqH1engIYcKgR58EhckxrdqXDXatCwviu95ZEd2EVbmfiae3BMSE2RIcBQR6YOjBQ5qrsW5DOBfNH9JG7jItiwCEAgMB5cIgcTmFVPT47kIl7p8XY5VT5jqJpuYZavRH+Hlr8+tClMldEUnCOWYuo25QCVxMncjRfJ2ZDIQi4dWKU3KU4NL3RsljkkexyeQshSTHgEAAOMiZyRDtOFeGS/v5ONT+LHG5qHAw9txNLFZD9Y8AhAJwHh8jRmMwiknMqMCbKV+5SHF6ot+X29Uo7nZGXuoYBhwBwHhwiR5NWVI26BhOG9LHfu1gcRdOCm8XVepkrISkx4BCAxtvEGXCIHEZydgUAdHodIWrJRW15KyypNshcCUmJAYcANI7B4Tw4RA4jOacCUX5u0HVi7htqXdPcP0XswXEqDDgEgIOMiRzNrjNFGBdtn1PkOyqDkX/lORNOnEAAGHCIHMnZ4hqkF9Xg8dmD5C7FacwdGoLYEE+5yyAJMeAQAMtEf7yLisgx7E0rgVIhYHL/1he7pM57K2Gk3CWQxHiJigBYBhnzLioix5BbXodATy3cNPwblagtDDgEwHKJivmGyDHkV9YjSOcidxlEdo0Bhyx4mziRwyiorEcwAw7RRTHgEIDGHhy5iyCiDjlbXINIPze5yyCyaww4BAAQwB4cIkdQ32BCTnkd+ga4y10KkV1jwCEAHIND5CgySmogikC/AA+5SyGyaww4BMBymzgDDpH9SyusAQD0ZcAhuigGHALAxTaJHMWx3Ar4uKnh666RuxQiu8aAQwCaxuDIXQURXUx9gwlfHMzClcND5S6FyO4x4BAALtVA5AjWH8pBWa0Bt0+KlrsUIrvHgEMAGsfgyF0EEbXJbBbxv93pmDU4GFH+vIOKqD0MOASASzUQ2bsdpwuRXlSDJVPYe0PUEQw4BKBpkLHcVRBRW75OzMbgEB1GRfrKXQqRQ2DAIQBNq4kz4RDZoxq9EdtOFeKqERxcTNRRDDgEABDApRqI7NXWU4WobzBj7tAQuUshchgMOATAMgbHzPvEiezSj0dyMTzcG+G+XH+KqKMYcAgAl2ogsldms4gdp4swJy5Y7lKIHAoDDgHgbeJE9koEYDCa4ceZi4k6hQGHAFjuouIgYyIichYMOATAslQD8w0RETkLBhwC0DjImAmHiIicBAMOAWic6E/uIoiIiCTCgEMAuFQDERE5FwYcsuBt4kRE5EQYcAgAx+AQEZFzYcAhAJalGjiRMREROQsGHAJg6cEBOA6HiIicAwMOAbDMZAxwLhwiInIODDgEwHKbOMDlGoiIyDkw4BAAy0zGAAcaExGRc2DAIQCW1cQBXqIiIiLnoJK7ALIPisaoyx6c7hNFEWYRMJlFmEURJrMIkyjCbD7/uanxc7MZ1q/NF2z3cdegj7er3E0hInJYDDgEwHKbONCxHhyx8Y24wSSiwWxGg9Fs+dxkbnyIMJrNMJlFGBvfsI2mxo9/3m4WYTJbnmP92mT+037Lm7/ZGg7Q7Otm25uCgnU7rMHCLF4YOJqCSNO+8881i7jg2ObHiaIlkFzYHmNj+xpM59smhbcTRkKtPN/J2nSHm2j9+oJ/PwFQKwUoFQqoFQKUCgEqpaJxmwC1UmH5qFBApRSgatxv2SdY9ymabqejdll+Bs5/zzeYzDAYm3/dbJ+p+c+KoWm/0fI9ZNl//nlN+w1Gs9xNJXJIDDgE4PxdVNet2mP9xW00i2gwmmFoDCzWX85ms80uZTW92aoUljdf60MQoBAAxYVfKxq3CeePa/r8YtvVSgVcFE3Pt2xTCgIEQYBSYXnehZ+ffwBKhWWfSiFYw4FKqbAEhgtCg/LC17Z+fb4mxQXHnK/Tsv+PjDK88PNJ/PWTQ7b5n3wRCgFQNYagpmCkavzoqlHCXaOEm0YFd+2fPmqUcNOq4KJSNGuf4k//L5QX/D8XBEusVigaA7blP+t2QbBsFxq3o/FrEecDZVOYvjBINNvXFCIav5fPB4nzz7EGjcaAYTCZG7/f/xRETE0/D5avpcixQuP3o6YxjKqVCsvXqvNfq5QKjO/ri+Hh3t1/QaJehAGHAADj+/ohYVwERADqxjc0tUrR/PM//RK+8HOVUmj8JW35XN0YUFTWwNI8uKgUApTK5tub3vR6u/gIH1w/OgwNJss76IX/S5o+bfr/dOHgcJNZRINZhKkxhFqDqkls7GUynz/mgl6zpmMs+8zW3rk/H1NrMKHWYESN3vKxqEqPcyVG1BhMqNVbPtY1mKw9anJc7bwwMKiavj8VAtSq80FN08r3slalgIdWZf0eVquElue5MIioFC32ay78uVApmv08tLVPyR4zIpthwCEAQJDOBS9cM1TuMqiRt5tG7hK6TWw2/uiCsUaNlwJFNI75svwHy6di40fL80Wx5XYBaBa+LwwgDAxE1IQBh4hsQhAsPXj8JUNEcuBt4kREROR0GHCIiIjI6TDgEBERkdNhwCEiIiKnw4BDRERETqdbAeell16CIAhYtmyZdduaNWswdepU6HQ6CIKA8vLybp8TAKZOnWqZAOyCxz333NOd8omIiMhJdTngHDx4EKtXr8awYcOaba+trcXs2bOxYsUKyc7ZZMmSJcjLy7M+XnnllS7VTkRERM6tSwGnuroaCQkJWLt2LXx8fJrtW7ZsGR5//HGMHz9esnM2cXNzQ3BwsPWh0+m6Uj4RERE5uS4FnHvvvRdz587FjBkzJCukI+f85JNP4O/vj7i4OCxfvhy1tbVtHqvX61FZWdnsQURERL1DpycZ/fzzz3Ho0CEcPHhQsiI6cs6bbroJkZGRCA0NxdGjR/HYY48hJSUF69evb/X4lStX4plnnpGsRiIiInIcnQo4WVlZePDBB7F582a4uLhIUkBHz3nXXXdZPx86dChCQkIwffp0pKWloV+/fi2OX758OR5++GHr15WVlQgPD5ekZiIiIrJvnQo4iYmJKCwsxMiRI63bTCYTdu3ahTfffBN6vR5KpbJTBXT1nOPGjQMApKamthpwtFottFptp2ohIiIi59CpgDN9+nQkJyc323bbbbdh0KBBeOyxxzodbrpzzqSkJABASEhIp1+TiIiInFunAo6npyfi4uKabXN3d4efn591e35+PvLz85GamgoASE5OhqenJyIiIuDr6wvAEmquueYa3HfffR06Z1paGj799FNcccUV8PPzw9GjR/HQQw9hypQpbd5S/meiKAIABxsTERE5kKb37ab38Y7q9CDj9qxatarZ4N4pU6YAANatW4fFixcDsASW4uLiDp9To9Fgy5YtePXVV1FTU4Pw8HAsWLAATzzxRIfPUVVVBQAch0NEROSAqqqq4OXl1eHjBbGzkchBmc1m5ObmwtPTE4IgyFJD00DnrKysXjmHT29uf29uO8D29+b29+a2A727/VK1XRRFVFVVITQ0FApFx2e3kbwHx14pFAqEhYXJXQYAQKfT9bpv9Av15vb35rYDbH9vbn9vbjvQu9svRds703PThIttEhERkdNhwCEiIiKnw4DTg7RaLZ566qleOz9Pb25/b247wPb35vb35rYDvbv9cre91wwyJiIiot6DPThERETkdBhwiIiIyOkw4BAREZHTYcAhIiIip8OAI6HTp09j/vz58Pf3h06nw+TJk7F9+3br/pKSEsyePRuhoaHQarUIDw/Hfffd1+H1sfR6PUaMGAFBEKyLjdoTW7X/qquuQkREBFxcXBASEoJbbrkFubm5tm5Op9mi/RkZGbjjjjsQHR0NV1dX9OvXD0899RQMBkNPNKnDbPVv/8ILL2DixIlwc3ODt7e3jVvRdbZqf2lpKRISEqDT6eDt7Y077rgD1dXVtm5Op7TX9iNHjmDhwoUIDw+Hq6srYmNj8dprr7V73kOHDmHmzJnw9vaGn58f7rrrLrtrO2C79rd3Xnthi/bv2LEDgiC0+jh48GCHa2PAkdC8efNgNBqxbds2JCYmYvjw4Zg3bx7y8/MBWGZTnj9/PjZs2IDTp0/j/fffx5YtW3DPPfd06PyPPvooQkNDbdmEbrFV+6dNm4Yvv/wSKSkp+Oabb5CWlobrrruuJ5rUKbZo/6lTp2A2m7F69WocP34c//3vf7Fq1SqsWLGip5rVIbb6tzcYDLj++uuxdOnSnmhGl9mq/QkJCTh+/Dg2b96MH3/8Ebt27cJdd93VE03qsPbanpiYiMDAQHz88cc4fvw4/v73v2P58uV488032zxnbm4uZsyYgZiYGOzfvx+bNm3C8ePHresZ2hNbtL8j57UXtmj/xIkTkZeX1+xx5513Ijo6GqNHj+54cSJJoqioSAQg7tq1y7qtsrJSBCBu3ry5zee99tprYlhYWLvn//nnn8VBgwaJx48fFwGIhw8flqJsydi6/Rf6/vvvRUEQRIPB0OV6pdaT7X/llVfE6OjoLtcqtZ5o+7p160QvL6/ulmoTtmr/iRMnRADiwYMHrds2btwoCoIg5uTkSFN8N3W17X/961/FadOmtbl/9erVYmBgoGgymazbjh49KgIQz5w5I03xErBV+7t63p5mq/b/mcFgEAMCAsRnn322U/WxB0cifn5+GDhwID788EPU1NTAaDRi9erVCAwMxKhRo1p9Tm5uLtavX49LL730oucuKCjAkiVL8NFHH8HNzc0W5XebLdt/odLSUnzyySeYOHEi1Gq1VOV3W0+1HwAqKirg6+srRdmS6Mm22yNbtX/v3r3w9vZu9hfrjBkzoFAosH//fsnb0RVdaTvQ/vewXq+HRqNptrCiq6srAGD37t3SNaCbbNX+rp63p9mq/X+2YcMGlJSU4LbbbutcgZ2KQ3RRWVlZ4qhRo0RBEESlUimGhISIhw4danHcjTfeKLq6uooAxCuvvFKsq6tr85xms1mcPXu2+Nxzz4miKIpnz561yx4cUbRN+5s8+uijopubmwhAHD9+vFhcXGyLJnSLLdvf5MyZM6JOpxPXrFkjZendZuu223MPjijapv0vvPCCOGDAgBbbAwICxLffflvS+rujo21v8vvvv4sqlUr85Zdf2jzm2LFjokqlEl955RVRr9eLpaWl4oIFC0QA4osvvmiLZnSZLdrflfPKxVbtv9CcOXPEOXPmdLo2Bpx2PPbYYyKAiz5Onjwpms1m8aqrrhLnzJkj7t69W0xMTBSXLl0q9unTR8zNzW12zry8PPHkyZPi999/Lw4ePFhcunRpm6//2muviZMmTRKNRqMoij0fcORuf5OioiIxJSVF/PXXX8VJkyaJV1xxhWg2m23VbCt7ab8oimJ2drbYr18/8Y477rBFU1uwp7bLEXDkbr+cAccWbRdFUUxOThb9/f2tf7BdzCeffCIGBQWJSqVS1Gg04iOPPCIGBQWJL730ki2a3Izc7e/seaUmd/svlJWVJSoUCvHrr7/udDsYcNpRWFgonjx58qIPvV4vbtmyRVQoFGJFRUWz58fExIgrV65s8/y//fabCKDNb9r58+eLCoVCVCqV1gcAUalUiosWLZK0ra2Ru/2tycrKEgGIe/bs6XK7Ospe2p+TkyP2799fvOWWW5qNS7Ale2m7KMoTcORu/7vvvit6e3s329bQ0CAqlUpx/fr13W/gRdii7cePHxcDAwPFFStWdKqW/Px8saqqSqyurhYVCoX45Zdfdrt97ZG7/V39npKK3O2/0LPPPisGBAR0acylqnMXtHqfgIAABAQEtHtcbW0tADS7Ztz0tdlsbvN5Tfv0en2r+19//XU8//zz1q9zc3Mxa9YsfPHFFxg3bly7dXWX3O2X6jldZQ/tz8nJwbRp0zBq1CisW7euxWvYij20XU5yt3/ChAkoLy9HYmKidTzDtm3bYDabbf6zL3Xbjx8/jssuuwy33norXnjhhU7VEhQUBAB477334OLigpkzZ3bq+V0hd/u7+j0lFbnb30QURaxbtw6LFi3q2pjLTkcialVRUZHo5+cnXnvttWJSUpKYkpIiPvLII6JarRaTkpJEURTFn376SXzvvffE5ORk8ezZs+KPP/4oxsbGipMmTbKeZ//+/eLAgQPF7OzsVl/HXsfg2Kr9+/btE9944w3x8OHDYkZGhrh161Zx4sSJYr9+/cT6+npZ2toaW7U/OztbjImJEadPny5mZ2eLeXl51oe9sOX3/rlz58TDhw+LzzzzjOjh4SEePnxYPHz4sFhVVdXj7WyLLds/e/ZsMT4+Xty/f7+4e/dusX///uLChQt7vI1t6Ujbk5OTxYCAAPHmm29u9v1bWFhoPU9rbX/jjTfExMREMSUlRXzzzTdFV1dX8bXXXuvxNl6MrdrfkfPaA1v++4uipScLjZfDuoIBR0IHDx4UL7/8ctHX11f09PQUx48fL/7888/W/du2bRMnTJggenl5iS4uLmL//v3Fxx57TCwrK7Mes337dhGAePbs2VZfw14Djijapv1Hjx4Vp02bJvr6+oparVaMiooS77nnnjYDoJxs0f5169a1eQ3cntjqe//WW29tte3bt2/vucZ1gK3aX1JSIi5cuFD08PAQdTqdeNttt9lVuBPF9tv+1FNPtfpvGBkZaT2mtbbfcsstoq+vr6jRaMRhw4aJH374YQ+2quNs1f72zmsvbNV+URTFhQsXihMnTuxybYIoimLn+32IiIiI7BfnwSEiIiKnw4BDRERETocBh4iIiJwOAw4RERE5HQYcIiIicjoMOEREROR0GHCIiIjI6TDgEBERkdNhwCEiIiKnw4BDRERETocBh4iIiJwOAw4RERE5nf8HCShctHrrQzkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block reads in a csv with whole districts to be cut out of the map\n",
      "For AZ my input file says to cut out 0 districts out of 9\n",
      "There were no cut districts in AZ\n"
     ]
    }
   ],
   "source": [
    "print(\"This code block reads in a csv with whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "                            if STATE == \"NC\":\n",
    "                                if t == 1828:\n",
    "                                    print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                    cutList.append(t)\n",
    "                                    cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 districts\n"
     ]
    }
   ],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]\n",
    "print(nDistricts,\"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before squishing in outcroppings.\n",
      "Of the total statePop 7151502.0 we ignore 0.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 0 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 9 rather than original 9\n",
      "Each district will be drawn to house 794611.333 = 1/ 9 of non-cutout state pop 7151502.0 vs original= 7151502.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "here is the AZ map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before squishing in outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 8 #11  #reduce for TX\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 5 #7 #reduce for TX\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "the max LAT-scaled distance between counties is 5.1691\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 5.1691  0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For corners and outcroppings, we fuse county clusters\n",
    "collapsedCountyList = list()  #VA has independent cities that we will reassign to parent counties\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "398fbc6d-3b05-4c1f-87cb-72d11cdd98bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are a total of 3 groups of counties to become corner clusters\n",
      "[[0], [6], [12]]\n"
     ]
    }
   ],
   "source": [
    "useCCBlist = True\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "CCBlistString = trimDF[\"CCB counties\"][DFrow]\n",
    "CCBlist = list(ast.literal_eval(CCBlistString))  #may otherwise be interpreted as a tuple\n",
    "print(\"There are a total of\",len(CCBlist),\"groups of counties to become corner clusters\")\n",
    "print(CCBlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will use the cutNClip csv list of corner-county bundles\n",
      "Our final pops and fused-county lists are...\n",
      "66021.0 [0]\n",
      "16557.0 [6]\n",
      "47669.0 [12]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aDP = float(statePop)/nDistricts  #this block modified from VRA-HD-MO\n",
    "if useCCBlist:\n",
    "    print(\"We will use the cutNClip csv list of corner-county bundles\")\n",
    "    nCCBs = len(CCBlist)\n",
    "    CCBpop, CCBgeom = [0.]*nCCBs, [countyGeom[L[0]] for L in CCBlist]\n",
    "    for i, L in enumerate(CCBlist):\n",
    "        for cc in L:\n",
    "            CCBpop[i] += countyPop[cc]\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[cc])\n",
    "    passThruList, rejectList = list(), list()\n",
    "else:\n",
    "    maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "    maxCCBpop = maxCCBratio * aDP\n",
    "    print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "    CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "    nFuses = 0\n",
    "    for c in set(has1neighborCs).difference(set(collapsedCountyList)):\n",
    "        print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "        if countyPop[c] > maxCCBpop:\n",
    "            plotPoly(countyGeom[c])\n",
    "            plotCenter(c,countyGeom[c])\n",
    "            nc = neighborCountyList[c][0]\n",
    "            plotPoly(countyGeom[nc],0.5)\n",
    "            plotPoly(MAP,0.2)\n",
    "            print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "            print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "            fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "            if fuseChoice == 0:\n",
    "                keepUnfused = True\n",
    "                break\n",
    "            elif int(fuseChoice) == 1:\n",
    "                CCBlist.append([c])\n",
    "                CCBpop.append(countyPop[c])  \n",
    "                hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "            elif int(fuseChoice) == 2:\n",
    "                CCBlist.append([c,nc ] )\n",
    "                CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "                hasFewNeighborCs.append(c)\n",
    "                passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "        else:\n",
    "            hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "            \n",
    "    for c in set(hasFewNeighborCs).difference(set(collapsedCountyList)):\n",
    "        print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "        if countyPop[c] < maxCCBpop :\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])\n",
    "            CCBno = CCBlist.index([c])\n",
    "        if c in passThruList:\n",
    "            CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "        if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "            CCBstarter = c\n",
    "            canAdd = True\n",
    "            while canAdd:\n",
    "                nbrSet = set()\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "                nPop = [countyPop[cc] for cc in nbrSet]\n",
    "                nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "                idx = np.argsort(nDist)\n",
    "                nbrList, newList = list(nbrSet), list()\n",
    "                for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                    cc = nbrList[idx[i]]\n",
    "                    if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                        newList.append(cc)\n",
    "                        CCBlist[CCBno].append(cc)\n",
    "                        CCBpop[CCBno] += countyPop[cc]\n",
    "                        #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "                if newList == list():  #no eligible neighbor counties found; stop\n",
    "                    canAdd = False\n",
    "                    for cc in CCBlist[CCBno]:\n",
    "                        plotPoly(countyGeom[cc])\n",
    "                        plotCenter(CCBno,countyGeom[cc])\n",
    "    plotPoly(MAP,0.2)\n",
    "    plt.show()\n",
    "                        \n",
    "    print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "    plotPoly(MAP,0.15)\n",
    "    for L in CCBlist:\n",
    "        print(CCBlist.index(L),\" \",L)\n",
    "        for c in L:\n",
    "            plotPoly(countyGeom[c])\n",
    "            plotCenter(c,countyGeom[c])\n",
    "            for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "                plotPoly(countyGeom[cc],0.4)\n",
    "                plotCenter(cc,countyGeom[cc],8)\n",
    "    plt.show()\n",
    "    rejectList = list()\n",
    "    print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "    print(\"You may suggest [ [3], [43] , [85] ] \")\n",
    "\n",
    "    for i,L in enumerate(CCBlist):\n",
    "        if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "            print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "            isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "            if not (isOK == 1 or isOK == str(1)) :\n",
    "                if isOK == 0 or isOK == str(0) :\n",
    "                    rejectList.append(L)\n",
    "                else:\n",
    "                    notGood = True\n",
    "                    while notGood:       \n",
    "                        Lnew = ast.literal_eval(isOK)        \n",
    "                        if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                            notGood = False\n",
    "                        else:\n",
    "                            print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                    CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                    CCBlist[i] = Lnew.copy()\n",
    "    for L in rejectList:\n",
    "        del CCBpop[ CCBlist.index(L)]\n",
    "        del CCBlist[CCBlist.index(L)]\n",
    "    stillAdding = True\n",
    "    while stillAdding:\n",
    "        addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "        if int(addMore) != 1:\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                print(\"OK, we'll stop adding corner clusters\")\n",
    "                stillAdding = False\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                        for L in CCBlist:\n",
    "                            if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                                notGood = True\n",
    "                                print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                                isOK = input(\"Try again here \")\n",
    "                        if notGood == False:\n",
    "                            CCBlist.append(Lnew)\n",
    "                            CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "    print(\"Our final pops and fused-county lists are...\")\n",
    "    allFusedCounties = list()\n",
    "    CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "    CCBcp = list()\n",
    "    for i, L in enumerate(CCBlist):\n",
    "        CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "        print(CCBpop[i],L)\n",
    "        pops, CPs = list(), list()\n",
    "        for c in L:\n",
    "            if c == L[0]:\n",
    "                CCBgeom[i] = countyGeom[c]\n",
    "            else:\n",
    "                CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "            allFusedCounties.append(c)\n",
    "            pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "            CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "            plotPoly(countyGeom[c])\n",
    "            plotCenter(i,countyGeom[c])\n",
    "    plotPoly(MAP,0.2)\n",
    "    plt.show()\n",
    "    \n",
    "    for i,L in enumerate(CCBlist):\n",
    "        if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "            print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "            isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "            if not (isOK == 1 or isOK == str(1)) :\n",
    "                if isOK == 0 or isOK == str(0) :\n",
    "                    rejectList.append(L)\n",
    "                else:\n",
    "                    notGood = True\n",
    "                    while notGood:       \n",
    "                        Lnew = ast.literal_eval(isOK)        \n",
    "                        if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                            notGood = False\n",
    "                        else:\n",
    "                            print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                    CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                    CCBlist[i] = Lnew.copy()\n",
    "    for L in rejectList:\n",
    "        del CCBpop[ CCBlist.index(L)]\n",
    "        del CCBlist[CCBlist.index(L)]\n",
    "    stillAdding = True\n",
    "    while stillAdding:\n",
    "        addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "        if int(addMore) != 1:\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                print(\"OK, we'll stop adding corner clusters\")\n",
    "                stillAdding = False\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                        for L in CCBlist:\n",
    "                            if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                                notGood = True\n",
    "                                print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                                isOK = input(\"Try again here \")\n",
    "                        if notGood == False:\n",
    "                            CCBlist.append(Lnew)\n",
    "                            CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15892\n",
      "We defined a total of 1 unit (but not corner-cluster) counties in the map\n",
      "excluding the cut-out districts and collapsed counties\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties < 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in set(uncutCountyList).difference(set(collapsedCountyList)):\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map\")\n",
    "print(\"excluding the cut-out districts and collapsed counties\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "10 fragmented VTDs out of 1538\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAGdCAYAAAAFcOm4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhkklEQVR4nO3dfVjV9eH/8ReIHOBS8C44qJg0neYtqKkHW9qiyHE12XY55+XCnLnV8JpGly660cu69sUrZ9aaebNmbCujXN5sZjqGojNRA6FEy3KakHGwZnKUDI3z/v3Rr1MnwTzIjW95Pq7rc12dz3l/znl/3hA+rw/ncIKMMUYAAAAWC27tCQAAAFwuggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9UJaewKXwuv16sMPP1THjh0VFBTU2tMBAACXwBij06dPq3v37goObt5rKFYEzYcffqi4uLjWngYAAGiEiooK9ezZs1mfw4qg6dixo6QvFiQyMrKVZwMAAC6Fx+NRXFyc79/x5mRF0Hz5a6bIyEiCBgAAy7TEy0V4UTAAALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsF1DQLFu2TEOGDPF9BIHL5dJrr7120WPWrFmj/v37KywsTIMHD9amTZsua8IAAADfFFDQ9OzZUwsXLlRxcbGKior0/e9/XxMmTNCBAwfqHb9r1y5NnjxZ06dPV0lJidLS0pSWlqaysrImmTwAAIAkBRljzOU8QJcuXbRo0SJNnz79gvsmTZqkmpoabdy40bdv9OjRSkhI0PLlyy/5OTwej6KiolRdXc2HUwIAYImW/Pe70a+hqaurU25urmpqauRyueodU1hYqOTkZL99KSkpKiwsvOhj19bWyuPx+G0AAAANCQn0gP3798vlcumzzz5Thw4dtG7dOg0YMKDesW63WzExMX77YmJi5Ha7L/oc2dnZWrBgQaBTa5TeD7zaIs/TlN5fmNraU2gz+P4AADsEfIWmX79+Ki0t1Z49e3Tvvfdq6tSpOnjwYJNOKisrS9XV1b6toqKiSR8fAABcXQK+QhMaGqo+ffpIkoYPH6433nhDTz31lFasWHHBWKfTqaqqKr99VVVVcjqdF30Oh8Mhh8MR6NQAAEAbddl/h8br9aq2trbe+1wul/Lz8/325eXlNfiaGwAAgMYI6ApNVlaWxo8fr169eun06dNavXq1CgoKtGXLFklSenq6evTooezsbEnSrFmzNHbsWC1evFipqanKzc1VUVGRVq5c2fRnAgAA2qyAgubEiRNKT09XZWWloqKiNGTIEG3ZskW33nqrJKm8vFzBwV9d9ElKStLq1av18MMP68EHH1Tfvn21fv16DRo0qGnPAgAAtGkBBc2f//zni95fUFBwwb6JEydq4sSJAU0KAAAgEHyWEwAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoBBU12drZuuOEGdezYUdHR0UpLS9OhQ4cuekxOTo6CgoL8trCwsMuaNAAAwNcFFDTbt29XRkaGdu/erby8PJ0/f1633XabampqLnpcZGSkKisrfduxY8cua9IAAABfFxLI4M2bN/vdzsnJUXR0tIqLi3XTTTc1eFxQUJCcTmfjZggAAPAtLus1NNXV1ZKkLl26XHTcmTNndO211youLk4TJkzQgQMHLjq+trZWHo/HbwMAAGhIo4PG6/Vq9uzZGjNmjAYNGtTguH79+mnVqlXasGGDnn/+eXm9XiUlJemDDz5o8Jjs7GxFRUX5tri4uMZOEwAAtAGNDpqMjAyVlZUpNzf3ouNcLpfS09OVkJCgsWPHau3atbrmmmu0YsWKBo/JyspSdXW1b6uoqGjsNAEAQBsQ0GtovjRz5kxt3LhRO3bsUM+ePQM6tn379kpMTNThw4cbHONwOORwOBozNQAA0AYFdIXGGKOZM2dq3bp12rp1q+Lj4wN+wrq6Ou3fv1+xsbEBHwsAAFCfgK7QZGRkaPXq1dqwYYM6duwot9stSYqKilJ4eLgkKT09XT169FB2drYk6dFHH9Xo0aPVp08fnTp1SosWLdKxY8d09913N/GpAACAtiqgoFm2bJkkady4cX77n3vuOd11112SpPLycgUHf3Xh55NPPtGMGTPkdrvVuXNnDR8+XLt27dKAAQMub+YAAAD/X0BBY4z51jEFBQV+t5csWaIlS5YENCkAAIBA8FlOAADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArBdQ0GRnZ+uGG25Qx44dFR0drbS0NB06dOhbj1uzZo369++vsLAwDR48WJs2bWr0hAEAAL4poKDZvn27MjIytHv3buXl5en8+fO67bbbVFNT0+Axu3bt0uTJkzV9+nSVlJQoLS1NaWlpKisru+zJAwAASFKQMcY09uCPPvpI0dHR2r59u2666aZ6x0yaNEk1NTXauHGjb9/o0aOVkJCg5cuXX9LzeDweRUVFqbq6WpGRkY2dbr16P/Bqkz5eS3h/YWprT6HN4PsDABqvOf/9/qbLeg1NdXW1JKlLly4NjiksLFRycrLfvpSUFBUWFl7OUwMAAPiENPZAr9er2bNna8yYMRo0aFCD49xut2JiYvz2xcTEyO12N3hMbW2tamtrfbc9Hk9jpwkAANqARgdNRkaGysrKtHPnzqacj6QvXny8YMGCJn9cAGjL+BUqrmaN+pXTzJkztXHjRm3btk09e/a86Fin06mqqiq/fVVVVXI6nQ0ek5WVperqat9WUVHRmGkCAIA2IqCgMcZo5syZWrdunbZu3ar4+PhvPcblcik/P99vX15enlwuV4PHOBwORUZG+m0AAAANCehXThkZGVq9erU2bNigjh07+l4HExUVpfDwcElSenq6evTooezsbEnSrFmzNHbsWC1evFipqanKzc1VUVGRVq5c2cSnAgAA2qqArtAsW7ZM1dXVGjdunGJjY33bSy+95BtTXl6uyspK3+2kpCStXr1aK1eu1NChQ/X3v/9d69evv+gLiQEAAAIR0BWaS/mTNQUFBRfsmzhxoiZOnBjIUwEAAFwyPssJAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgvYCDZseOHbrjjjvUvXt3BQUFaf369RcdX1BQoKCgoAs2t9vd2DkDAAD4CThoampqNHToUC1dujSg4w4dOqTKykrfFh0dHehTAwAA1Csk0APGjx+v8ePHB/xE0dHR6tSpU8DHAQAAfJsWew1NQkKCYmNjdeutt+r111+/6Nja2lp5PB6/DQAAoCHNHjSxsbFavny5XnnlFb3yyiuKi4vTuHHjtG/fvgaPyc7OVlRUlG+Li4tr7mkCAACLBfwrp0D169dP/fr1891OSkrSf//7Xy1ZskR/+9vf6j0mKytLmZmZvtsej4eoAQAADWr2oKnPyJEjtXPnzgbvdzgccjgcLTgjAABgs1b5OzSlpaWKjY1tjacGAABXoYCv0Jw5c0aHDx/23T569KhKS0vVpUsX9erVS1lZWTp+/Lj++te/SpKefPJJxcfHa+DAgfrss8/07LPPauvWrfrXv/7VdGcBAADatICDpqioSDfffLPv9pevdZk6dapycnJUWVmp8vJy3/3nzp3T/fffr+PHjysiIkJDhgzRv//9b7/HAAAAuBwBB824ceNkjGnw/pycHL/bc+fO1dy5cwOeGAAAwKXis5wAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWCzhoduzYoTvuuEPdu3dXUFCQ1q9f/63HFBQUaNiwYXI4HOrTp49ycnIaMVUAAID6BRw0NTU1Gjp0qJYuXXpJ448eParU1FTdfPPNKi0t1ezZs3X33Xdry5YtAU8WAACgPiGBHjB+/HiNHz/+kscvX75c8fHxWrx4sSTp+uuv186dO7VkyRKlpKQE+vQAAAAXaPbX0BQWFio5OdlvX0pKigoLCxs8pra2Vh6Px28DAABoSMBXaALldrsVExPjty8mJkYej0dnz55VeHj4BcdkZ2drwYIFzT01a/V+4NXWnkLA3l+Y2tpTAIAWwc/o1nFFvsspKytL1dXVvq2ioqK1pwQAAK5gzX6Fxul0qqqqym9fVVWVIiMj6706I0kOh0MOh6O5pwYAAK4SzX6FxuVyKT8/329fXl6eXC5Xcz81AABoIwIOmjNnzqi0tFSlpaWSvnhbdmlpqcrLyyV98eui9PR03/h77rlHR44c0dy5c/XOO+/omWee0csvv6z77ruvac4AAAC0eQEHTVFRkRITE5WYmChJyszMVGJioubNmydJqqys9MWNJMXHx+vVV19VXl6ehg4dqsWLF+vZZ5/lLdsAAKDJBPwamnHjxskY0+D99f0V4HHjxqmkpCTQpwIAALgkV+S7nAAAAAJB0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6jQqapUuXqnfv3goLC9OoUaO0d+/eBsfm5OQoKCjIbwsLC2v0hAEAAL4p4KB56aWXlJmZqfnz52vfvn0aOnSoUlJSdOLEiQaPiYyMVGVlpW87duzYZU0aAADg6wIOmieeeEIzZszQtGnTNGDAAC1fvlwRERFatWpVg8cEBQXJ6XT6tpiYmMuaNAAAwNcFFDTnzp1TcXGxkpOTv3qA4GAlJyersLCwwePOnDmja6+9VnFxcZowYYIOHDjQ+BkDAAB8Q0BB8/HHH6uuru6CKywxMTFyu931HtOvXz+tWrVKGzZs0PPPPy+v16ukpCR98MEHDT5PbW2tPB6P3wYAANCQZn+Xk8vlUnp6uhISEjR27FitXbtW11xzjVasWNHgMdnZ2YqKivJtcXFxzT1NAABgsYCCplu3bmrXrp2qqqr89ldVVcnpdF7SY7Rv316JiYk6fPhwg2OysrJUXV3t2yoqKgKZJgAAaGMCCprQ0FANHz5c+fn5vn1er1f5+flyuVyX9Bh1dXXav3+/YmNjGxzjcDgUGRnptwEAADQkJNADMjMzNXXqVI0YMUIjR47Uk08+qZqaGk2bNk2SlJ6erh49eig7O1uS9Oijj2r06NHq06ePTp06pUWLFunYsWO6++67m/ZMAABAmxVw0EyaNEkfffSR5s2bJ7fbrYSEBG3evNn3QuHy8nIFB3914eeTTz7RjBkz5Ha71blzZw0fPly7du3SgAEDmu4sAABAmxZw0EjSzJkzNXPmzHrvKygo8Lu9ZMkSLVmypDFPAwAAcEn4LCcAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1GhU0S5cuVe/evRUWFqZRo0Zp7969Fx2/Zs0a9e/fX2FhYRo8eLA2bdrUqMkCAADUJ+Cgeemll5SZman58+dr3759Gjp0qFJSUnTixIl6x+/atUuTJ0/W9OnTVVJSorS0NKWlpamsrOyyJw8AACA1ImieeOIJzZgxQ9OmTdOAAQO0fPlyRUREaNWqVfWOf+qpp3T77bdrzpw5uv766/XYY49p2LBh+uMf/3jZkwcAAJCkkEAGnzt3TsXFxcrKyvLtCw4OVnJysgoLC+s9prCwUJmZmX77UlJStH79+gafp7a2VrW1tb7b1dXVkiSPxxPIdC+Jt/bTJn9MXKg5vnYtwcbvD1vXGs2P7+eWwTpf+LjGmGZ5/K8LKGg+/vhj1dXVKSYmxm9/TEyM3nnnnXqPcbvd9Y53u90NPk92drYWLFhwwf64uLhAposrSNSTrT2DtoO1xtWE7+eW0dzrfPr0aUVFRTXrcwQUNC0lKyvL76qO1+vVyZMn1bVrVwUFBQX0WB6PR3FxcaqoqFBkZGRTTxUXwdq3Hta+9bD2rYe1bz0Nrb0xRqdPn1b37t2bfQ4BBU23bt3Url07VVVV+e2vqqqS0+ms9xin0xnQeElyOBxyOBx++zp16hTIVC8QGRnJN3grYe1bD2vfelj71sPat5761r65r8x8KaAXBYeGhmr48OHKz8/37fN6vcrPz5fL5ar3GJfL5TdekvLy8hocDwAAEKiAf+WUmZmpqVOnasSIERo5cqSefPJJ1dTUaNq0aZKk9PR09ejRQ9nZ2ZKkWbNmaezYsVq8eLFSU1OVm5uroqIirVy5smnPBAAAtFkBB82kSZP00Ucfad68eXK73UpISNDmzZt9L/wtLy9XcPBXF36SkpK0evVqPfzww3rwwQfVt29frV+/XoMGDWq6s7gIh8Oh+fPnX/ArLDQ/1r71sPath7VvPax967kS1j7ItMR7qQAAAJoRn+UEAACsR9AAAADrETQAAMB6BA0AALDeVR80S5cuVe/evRUWFqZRo0Zp7969rT0lq2RnZ+uGG25Qx44dFR0drbS0NB06dMhvzGeffaaMjAx17dpVHTp00E9+8pML/phieXm5UlNTFRERoejoaM2ZM0eff/6535iCggINGzZMDodDffr0UU5OTnOfnlUWLlyooKAgzZ4927ePtW8+x48f189//nN17dpV4eHhGjx4sIqKinz3G2M0b948xcbGKjw8XMnJyXrvvff8HuPkyZOaMmWKIiMj1alTJ02fPl1nzpzxG/PWW2/pe9/7nsLCwhQXF6fHH3+8Rc7vSlVXV6dHHnlE8fHxCg8P13e+8x099thjfp8FxNo3jR07duiOO+5Q9+7dFRQUdMFnLLbkOq9Zs0b9+/dXWFiYBg8erE2bNgV+QuYqlpuba0JDQ82qVavMgQMHzIwZM0ynTp1MVVVVa0/NGikpKea5554zZWVlprS01PzgBz8wvXr1MmfOnPGNueeee0xcXJzJz883RUVFZvTo0SYpKcl3/+eff24GDRpkkpOTTUlJidm0aZPp1q2bycrK8o05cuSIiYiIMJmZmebgwYPm6aefNu3atTObN29u0fO9Uu3du9f07t3bDBkyxMyaNcu3n7VvHidPnjTXXnutueuuu8yePXvMkSNHzJYtW8zhw4d9YxYuXGiioqLM+vXrzZtvvml++MMfmvj4eHP27FnfmNtvv90MHTrU7N692/znP/8xffr0MZMnT/bdX11dbWJiYsyUKVNMWVmZefHFF014eLhZsWJFi57vleR3v/ud6dq1q9m4caM5evSoWbNmjenQoYN56qmnfGNY+6axadMm89BDD5m1a9caSWbdunV+97fUOr/++uumXbt25vHHHzcHDx40Dz/8sGnfvr3Zv39/QOdzVQfNyJEjTUZGhu92XV2d6d69u8nOzm7FWdntxIkTRpLZvn27McaYU6dOmfbt25s1a9b4xrz99ttGkiksLDTGfPE/TXBwsHG73b4xy5YtM5GRkaa2ttYYY8zcuXPNwIED/Z5r0qRJJiUlpblP6Yp3+vRp07dvX5OXl2fGjh3rCxrWvvn89re/NTfeeGOD93u9XuN0Os2iRYt8+06dOmUcDod58cUXjTHGHDx40Egyb7zxhm/Ma6+9ZoKCgszx48eNMcY888wzpnPnzr6vxZfP3a9fv6Y+JWukpqaaX/ziF377fvzjH5spU6YYY1j75vLNoGnJdf7pT39qUlNT/eYzatQo86tf/Sqgc7hqf+V07tw5FRcXKzk52bcvODhYycnJKiwsbMWZ2a26ulqS1KVLF0lScXGxzp8/77fO/fv3V69evXzrXFhYqMGDB/t96npKSoo8Ho8OHDjgG/P1x/hyDF8rKSMjQ6mpqResD2vffP7xj39oxIgRmjhxoqKjo5WYmKg//elPvvuPHj0qt9vtt25RUVEaNWqU39p36tRJI0aM8I1JTk5WcHCw9uzZ4xtz0003KTQ01DcmJSVFhw4d0ieffNLcp3lFSkpKUn5+vt59911J0ptvvqmdO3dq/Pjxklj7ltKS69xUP4Ou2qD5+OOPVVdX5/eDXJJiYmLkdrtbaVZ283q9mj17tsaMGeP7S89ut1uhoaEXfHjo19fZ7XbX+3X48r6LjfF4PDp79mxznI4VcnNztW/fPt9HiXwda998jhw5omXLlqlv377asmWL7r33Xv3mN7/RX/7yF0lfrd3Ffr643W5FR0f73R8SEqIuXboE9PVpax544AH97Gc/U//+/dW+fXslJiZq9uzZmjJliiTWvqW05Do3NCbQr0PAH32AtisjI0NlZWXauXNna0+lTaioqNCsWbOUl5ensLCw1p5Om+L1ejVixAj93//9nyQpMTFRZWVlWr58uaZOndrKs7u6vfzyy3rhhRe0evVqDRw4UKWlpZo9e7a6d+/O2uOirtorNN26dVO7du0ueMdHVVWVnE5nK83KXjNnztTGjRu1bds29ezZ07ff6XTq3LlzOnXqlN/4r6+z0+ms9+vw5X0XGxMZGanw8PCmPh0rFBcX68SJExo2bJhCQkIUEhKi7du36w9/+INCQkIUExPD2jeT2NhYDRgwwG/f9ddfr/Lycklfrd3Ffr44nU6dOHHC7/7PP/9cJ0+eDOjr09bMmTPHd5Vm8ODBuvPOO3Xffff5rlKy9i2jJde5oTGBfh2u2qAJDQ3V8OHDlZ+f79vn9XqVn58vl8vVijOzizFGM2fO1Lp167R161bFx8f73T98+HC1b9/eb50PHTqk8vJy3zq7XC7t37/f7xs/Ly9PkZGRvn80XC6X32N8OaYtf61uueUW7d+/X6Wlpb5txIgRmjJliu+/WfvmMWbMmAv+PMG7776ra6+9VpIUHx8vp9Ppt24ej0d79uzxW/tTp06puLjYN2br1q3yer0aNWqUb8yOHTt0/vx535i8vDz169dPnTt3brbzu5J9+umnfh9wLEnt2rWT1+uVxNq3lJZc5yb7GRTQS4gtk5ubaxwOh8nJyTEHDx40v/zlL02nTp383vGBi7v33ntNVFSUKSgoMJWVlb7t008/9Y255557TK9evczWrVtNUVGRcblcxuVy+e7/8q3Dt912myktLTWbN28211xzTb1vHZ4zZ455++23zdKlS9v8W4fr8/V3ORnD2jeXvXv3mpCQEPO73/3OvPfee+aFF14wERER5vnnn/eNWbhwoenUqZPZsGGDeeutt8yECRPqfUtrYmKi2bNnj9m5c6fp27ev31taT506ZWJiYsydd95pysrKTG5uromIiGhTbx3+pqlTp5oePXr43ra9du1a061bNzN37lzfGNa+aZw+fdqUlJSYkpISI8k88cQTpqSkxBw7dswY03Lr/Prrr5uQkBDz+9//3rz99ttm/vz5vG27Pk8//bTp1auXCQ0NNSNHjjS7d+9u7SlZRVK923PPPecbc/bsWfPrX//adO7c2URERJgf/ehHprKy0u9x3n//fTN+/HgTHh5uunXrZu6//35z/vx5vzHbtm0zCQkJJjQ01Fx33XV+z4EvfDNoWPvm889//tMMGjTIOBwO079/f7Ny5Uq/+71er3nkkUdMTEyMcTgc5pZbbjGHDh3yG/O///3PTJ482XTo0MFERkaaadOmmdOnT/uNefPNN82NN95oHA6H6dGjh1m4cGGzn9uVzOPxmFmzZplevXqZsLAwc91115mHHnrI722/rH3T2LZtW70/36dOnWqMadl1fvnll813v/tdExoaagYOHGheffXVgM8nyJiv/flFAAAAC121r6EBAABtB0EDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAev8Pj2eTWj74R6MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 10 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "looking for surrounds, now working on vtd 500\n",
      "looking for surrounds, now working on vtd 1000\n",
      "looking for surrounds, now working on vtd 1500\n",
      "After surround checks, we appear to have 1460 building blocks defined. We captured 7 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            #if STATE == \"TN\":  #Tennessee has a lot of discontiguous county fragments.  Force the nearest intracounty to be a neighbor\n",
    "            #    if nVTDneighbors[v] == 0:\n",
    "            #        listMinusSelf = list( countyFragVTDset[c].difference({v}) )\n",
    "            #        fragDist = [fragVTDgeom[vvv].distance(fragVTDgeom[v]) for vvv in listMinusSelf ]\n",
    "            #        minIndex = fragDist.index(np.min(fragDist))\n",
    "            #        vv = listMinusSelf[minIndex]\n",
    "            #        nVTDneighbors[v] +=1\n",
    "            #        lastVTDneighbor[v] = vv\n",
    "            #        print(\"I am forcing an intracounty neighbor for VTDfrag\",v,\"of vtd\",V,\"in county\",c,\"to be VTDfrag\",vv)\n",
    "            #        forcedPairList.append([v,vv])\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[c])\n",
    "                                plotPoly(countyGeom[cc],0.2)\n",
    "                                plotCenter(cc, countyGeom[cc])\n",
    "                                plt.show()\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                if STATE == \"TN\":  #for TN, we tolerate some discontiguous counties\n",
    "                                    print(\"vtd fragment\",v,\"with no neighbors in its county\",countyNo[parentVTDno[v]],\n",
    "                                          \"borders a unit or fused county\",cc)\n",
    "                                else:\n",
    "                                    raise Exception(\"ERROR! Neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "if STATE == \"PA\":  #state-specific manual surround enforcement\n",
    "    specialSurrounded = [7240, 7701]\n",
    "    specialSurrounder = 6447\n",
    "    print(\"special for\",STATE,\", I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "    c = countyNo[parentVTDno[specialSurrounder]]\n",
    "    plotPoly(countyGeom[c], 0.5)\n",
    "    for v in specialSurrounded:\n",
    "        plotPoly(fragVTDgeom[v])\n",
    "        plotCenter(v,fragVTDgeom[v])\n",
    "    plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "    plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "    plt.show()\n",
    "    shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "    if shouldIcombine == 1:\n",
    "        for v in specialSurrounded:\n",
    "            vv = specialSurrounder\n",
    "            if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                for sNo, s in enumerate(surroundedVTDs):\n",
    "                    if surrounders[sNo] == v:\n",
    "                        surrounders[sNo] = vv\n",
    "            surroundedVTDs.append(v)\n",
    "            surrounders.append(vv)\n",
    "            nVTDneighbors[vv] -=1\n",
    "            nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "            fragVTDpop[vv] += fragVTDpop[v]\n",
    "            fragVTDpop[v] = 0\n",
    "\n",
    "\n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. unsurrounded vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, this unit appears surrounded.  Next block to confirm it has non-intracounty neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            if STATE != \"WA\" or i * ii != 3 :  #In WA, we don't allow ferry connection of Island to Clallam+Jefferson\n",
    "                unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "                unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94741893-6835-45ac-9793-64cbaf4823b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for L in sketchyList:  #hope that some of these flagged units picked up unit-county or CCB neighbors\n",
    "    print(\"sketchy unit\",L[0], \"has neighbor list\",unitNbrs[L[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should usually be the case, but exception in VA\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "if STATE == \"WA\":\n",
    "    print(\"adding a border unit between Island and Jefferson Counties, as these are only ferry-connected\")\n",
    "    borderUnits.append(2647)\n",
    "    borderType.append(\"vtd\")\n",
    "\n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 48\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "        print(\"above is with unit numbers; below is VTDfrag nos\")\n",
    "        for uu in smallPieceList:\n",
    "            vv = allUnits[uu]\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"v\"+str(vv),unitGeom[uu],8)\n",
    "        v = allUnits[u]\n",
    "        plotCenter(v,unitGeom[u],8)\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fc1ae6e1-ef52-412b-a966-e9739071af8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For safekeeping, write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"15Jul24\"\n",
    "print(\"For safekeeping, write the unit topology to a file\")  \n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno, \"allUnits\":allUnits} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d4baaea4-7af4-4407-aeb3-f4291d335f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy()  #optional reset if we need to rerun the below clipPoly routine with alternate clipPolys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "abdd07e7-f9be-480a-9d3d-b3948eeaaa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #default; if we did not have any clipPoly's to move in\n",
    "nClips = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4d5fdc3c-6126-4fa8-b484-1d0052c70aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now back to polygonal HD drawing prep ... optional squish\n",
      "for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state AZ\n",
      "clipPoly 0 intersects [14] counties and a total of 36 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGdCAYAAAAxCSikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACUtklEQVR4nOz9eXxkV3ng/3/uUruqSvvWWnvvtrttY2ywMWBjvEFYTDKQhZgJJmAS4BuYkIwnkwR+xDEBh4RkJoYhDDEhBAaIg8NmINA2GLxv7a13qdXa19q3e+/5/VFaWypJpa1K0vPmJdy6devWKal073PPec5zNKWUQgghhBCijOmlboAQQgghxFIkYBFCCCFE2ZOARQghhBBlTwIWIYQQQpQ9CViEEEIIUfYkYBFCCCFE2ZOARQghhBBlTwIWIYQQQpQ9s9QNWAuO49DX10cwGETTtFI3RwghhBDLoJQiFovR3NyMri/eh7IlApa+vj5aW1tL3QwhhBBCrEBPTw8tLS2L7rMlApZgMAjk33AoFCpxa4QQQgixHNFolNbW1unr+GK2RMAyNQwUCoUkYBFCCCE2meWkc0jSrRBCCCHKngQsQgghhCh7ErAIIYQQouxJwCKEEEKIsicBixBCCCHKngQsQgghhCh7ErAIIYQQouxJwCKEEEKIsicBixBCCCHKngQsQgghhCh7ErAIIYQQouxJwCKEEEKIsicBixBCCCHK3pZYrVkIIcTmpZTi3HgKy1E4SrGj0ofXZZS6WaLMSMAihBCiZM6NJ8nZipYqHy4j3+nfM5YkYzlUB9xUB9wlbqEoFxKwCCGEKBnLVnTWBuZsa632AzAaz3ByKI7XpdNS5S9F80QZkYBFCCFEWaqp8FBT4SGVtTk1HEcDOmoC6LpW6qaJEpCARQghRFnzuQ121VXgOIqu0QQKaAp78bvlEradyG9bCCFESSil0IroLNF1jZ11FQD0TaToj6Sp8kuey3Yh05qFEEKUxHAsQ13Qs6LnNlf62FVXwWg8s8atEuVKAhYhhBAlkcrZqx7WmZpZJLY++U0LIYTYtFSpGyA2jAQsQgghSkKtQbRRH/TQN5Fa/YFE2ZOARQghxKYV8JikczaW7ZS6KWKdScAihBBiU+usDXBmJFHqZoh1JgGLEEKIDZfO2bjNtbkEaZpGpd/NWCK7JscT5UkCFiGEEBtuMJqmMeRds+PVBT0yxXmLk4BFCCHEhlOKNS+x31bjp0uGhrYsCViEEEJsCR7TwNA1Ulm71E0R60ACFiGEEFtGa7Wf/ohMc96KJGARQgghRNmTgEUIIcSGUkqta4XasM9FJJVbx1cQpSABixBCiA01lshSU7F+KyzXVHgYlynOW44ELEIIITZULG0R8rrW9TVkjaGtRwIWIYQQW87aTpgW5UACFiGEEBtGKUXGWv91f1ymTtdIQqrfbiESsAghhNgQ0XSO44NxdtUF1v21dlT66KgNkLFsukelmNxWIAGLEEKIdXd2NEk8bbGvMYhpbNylpynsoz7o5cX+6Ia9plgfErAIIYRYN+mczbGBGHVBD82VvpK0wec26KwN0DshBeU2M7PUDRBCCLE19UdSZC2HfY3BUjcFt6GT24DcGbF+pIdFCCHEmspaDicGY/hdJu0165+vshxnRhO0VftL3QyxCtLDIoQQYs2cG09i2Yo9DaXvVQGIZywGo2nchr7mq0OLjSUBixBCiFWLpXMMRNLsqPLhd5fHpaVnLIlpaOyqqyh1U8QaKI9PlRBCiE1JKcXpkQQ+l1E2vSoAjqPI2Q6t1RKsbBUSsAghhFiR0XiGsUSWztrAhk5VXo7usSQdZZI/I9ZGeX3ChBBClD3Ldjg+GANgT8PG1lUphuSsbC3SwyKEEGLZ+iZSpHM2e+or0LTyDQjKt2VipSRgEUIIsaRExqJ3IkVT2FuyAnBie5OARQghxKK6RxMYusbeMkqqXUoZd/6IFZKARQghxIIiyRxDsTSt1X68LqPUzRHbnAQsQggh5lBKcWo4QdBrltVUZbG9ScAihBACyAcqvRMpUlmbnXUVGDLLRpQRCViEEGIbU0pxbjxFzs4vDNhc6ZPhH1GWJGARQohtRilFz1gKWykAdlT6cJvlWUtFiCkSsAghxDbgOIqzY0kcpdA0jZYqH64yLfi2FiZjMbGFSMAihBBbVDpn0zeRAkDXNFqr/ZKXIjYtCViEEGILiSRzjCWzALhNnc7aQFlXpBViuSRgEUKILaJnLIlpaHTWyqJ/YuuRgEUIITY5y3Y4ORynrdqP3y2ndbE1ySdbCCE2sfFEltFEln0NQRn6EVuaBCxCCLFJ9UdS6JrG7vqKUjdFiHUnAYsQQmxCg9E0pq5TF/SUuilCbIitOwlfCCG2qKFYGkCCFbGtSMAihBCbyHAsg+0oGkLeUjdFiA0lAYsQQmwSfRMpFIqmsK/UTRFiwxUVsNx9990cPnyYUChEKBTiiiuu4Pvf/z4AY2NjfPCDH2Tfvn34fD7a2tr40Ic+RCQSWfSY8XicD3zgA7S0tODz+Th48CCf+9znVv6OhBBiC0rnbBylqA9Kz4rYnopKum1paeGTn/wke/bsQSnFPffcw1ve8haeeuoplFL09fVx1113cfDgQbq7u7ntttvo6+vjm9/8ZsFjfuQjH+EnP/kJX/nKV+jo6OCHP/whv/d7v0dzczNvfvObV/0GhRBiKzgzkmBvQ7DUzRCiZDSlVrdEVHV1NZ/+9Ke59dZb5z32jW98g3e+850kEglMc+HY6MILL+Qd73gHf/qnfzq97dJLL+Wmm27iL/7iL5bVhmg0SjgcJhKJEAqFVvZGhBCiDFm2w4mhOJ21Abwuo9TN2TS6RhJ0SMXfslfM9XvFOSy2bfO1r32NRCLBFVdcseA+Uw0oFKwAXHnlldx333309vailOKnP/0px48f5/rrry/4nEwmQzQanfMlhBBbzWA0TfdYkv2NQQlWxLZXdB2Wo0ePcsUVV5BOp6moqODee+/l4MGD8/YbGRnhE5/4BO9973sXPd7f//3f8973vpeWlhZM00TXdb7whS/wmte8puBz7rzzTj7+8Y8X23QhhNgU0jmbs2NJ6oMeGkJSFE4IWMGQUDab5ezZs0QiEb75zW/yj//4jzzwwANzgpZoNMp1111HdXU19913Hy6Xq+Dx7rrrLr7whS9w11130d7ezoMPPsjtt9/Ovffey+tf//oFn5PJZMhkMnNer7W1VYaEhBCbmmU7dI0m8Zg6rdX+UjdnU5Mhoc2hmCGhVeewvP71r2fXrl18/vOfByAWi3HDDTfg9/v5zne+g9dbOKM9lUoRDoe59957eeMb3zi9/T3veQ/nzp3jBz/4wbLaIDksQojNrmcsieUoOmr8sibQGpCAZXMo5vq96tL8juNM93ZEo1FuuOEGPB4P991336LBCkAulyOXy6Hrc1NpDMPAcZzVNk0IITaF08NxGsNeWWlZiEUU9ddx++23c9NNN9HW1kYsFuOrX/0qR44c4f777ycajXL99deTTCb5yle+MicZtq6uDsPIJ4zt37+fO++8k5tvvplQKMRrX/taPvrRj+Lz+Whvb+eBBx7gy1/+Mp/5zGfW/t0KIUSZGYqlqfS7JVgRBT11dpzqgHv6+6lxEU2b+feUhbathUgqx0WtlWt/4CIU9RcyNDTELbfcQn9/P+FwmMOHD3P//fdz3XXXceTIER555BEAdu/ePed5Z86coaOjA4Bjx47NKSb3ta99jdtvv53f+q3fYmxsjPb2du644w5uu+22Vb41IYQof9FUjt31Ul9FFFbld9NeU9rhrdPD8ZK+PqxBDks5kBwWIcRmdG48SW2FR6Ysr4OtlMNS6veSyFgMRNPsqlv7GWsbUodFCCHE6li2kmBFlL31ClaKJQGLEEKUiEwGEuVOKYXjlMdAjAQsQghRQltgVF5sYb0TqZLnz0yRgEUIIUpIaq6IclbldzOayCy94waQgEUIIYQQCwp4TBIZC7sMhoUkYBFCiBKR0aD1s5U6rkr9XnbVVXBmpPTTmiVgEUKIEpF4Zf1spWCw1O9F0zRMvfThQulbIIQQQoiyVupeHpCARQghSsaWNdPEJlEGKSwSsAghRKmUQze7EMtRBh0sErAIIUSplMFN65ZVDkMYa0EptWXey2pJwCKEECUiRePEUpQCXSIWQAIWIYQomZqAh8FoutTNEGXMkR6WaWapGyCEENtV2O9iZDhDje1gGnL/uJY0NLpHE6Vuxqo5Cqr97lI3A6sMEsQlYBFCiBLaWRvg2GCM/Y2hUjdlS2mr8Ze6CVtKOSSIl74FQgixjWmaRkdNYEv0BgixniRgEUKIEvO6DCxH4ZRDsQshypQELEIIUQY6awKcHUuWuhlCLKgcEn8lh0UIIcqArmtSl2UVMnaGt//H2+lP9GPqJo5ySOTyw2xX7biKu157FwFXoMStFKshAYsQQpSJMriJLXtZO8vfPfl3JKx8MKJN/tTSVprTkdNc3ng5V+24it54L18/9nUAft77c+7vup/TE6cZz4zzsvqX8fLGl9MWbEMrh66DTaAcSgZJwCKEEGXC0DUsmeKcl0tDtHfme9MD4RZ6Yj3c88I905vrffXU+mtRSnFJ/SXccdUdNAYaGUuP8cTgE5ycOAnAn//iz6nz1VHpreQ7p7+Do/LTdH9z/29y+ytu39C3thmVQbwiAYsQQpSLxrCX/kia1uptPiU30gt2Fmp2zWzLJmHwedqqOmgNttIT6wHgH17/D+yr3jfz1PvuY+CZZ0HT+L+730Huv7yOo8NH2Vm5k45QB6PpUf70oT/l570/B5BhomUqh34oCViEEKJMuAwda7vPFMomwbGgunPudrcfGi4g2//0dLByUd1F7KjYMfPUc+fInu2h8U//JwBdn/wE2R9ZdL72SoB878zz90wHKweqD/DLvl/y056f8jdX/w0d4Y71f3/rzHYUveMpnAXGcDQtP7SjyAcgIZ+L6kC+KJ1lO/RNpKkLevC5jY1t9DJJwCKEEGWkHO5kSyrWP7dn5TwffuLTAHzm6s9wXft1cx7TAwGswUGcVApbg+8/8w3+w3TomVj4p+o1vcSyMU5OnOSpoac2bcDiOIrTIwlMXUPXNHZU+TD0pT9JkWSOMyP5XKBk1qKt2s9EKstgND9cZitFXdBDyOta1/YvlwQsQgixjgYTg2TszLL3700k6Kg9sI4tKnMV9fkhofCOBR8+GN7FL0ef5Z+e/yd0Tec1La/BpecvqGZVFVW//U6G/9f/wrYtfnCRwxtf/36ubL4SRzk4ykEpxf/30/+PeC7OU0NPTR83mo1uyNsrxsmhGIau01Llw1UgrymSytEfSbGvIVh0AnHY7yLsnxuMBM8LTvojKYaiaeqC3uIavw40tQWWC41Go4TDYSKRCKGQlLcWQpSPs9GztIXalr3/Q90v8ar2/evYok1gvAsMD4SaZrY5NgwfQ1W28ZOBR/jnF/+ZJwafwKW76Ax38oqmV/CGzjdwQc0FaJpG2kpz2b9cxseu+Bi/uvdX5xz+bPQsQ8khIF9pWNd0Lqy5EJdRHj0JAD1jSaoDbvxug3PjKezJoUJFfpVvTdOwHYcKj4vGcOmDiZUq5votPSxCCFFGDJlmC1UdkJqA0VNzt9cfQNM0rm2/lmvbr+WF0Rd4euhpjo0f43unv8c/v/DPtAZb+ew1n6Uj1AHAV178yryApS3UVlQQudGyloPtKAKe/CV62ydhT5KARQghyshCyZLbkq8y/7WIgzUHOVhzEADLsXh88HH+9KE/5UM/+RA1vhoAfmP/b6xzQ9fe2bEku+srSt2MsiOT/YUQQmx6pm7yyqZX8o5976A33ovlWHz2ms/y9n1vL3XTiraMfNltSXpYhBBiAUopFGo6UdNhJmnTUQ4Os/6tnOl9z98/nosX9brSw7I67zn0Ht5z6D2lbsaK9YwlCfvKJ5emnEjAIoTYtk6Mn8BrFE5YnErI1NCm/z31/dS/Dd3AhSu/fWof9DnPXa6B2DhBj28t3prYhLpHE1QH3PNm6og8CViEENuWx/DQGmotdTOmvTjUwzW7Dpe6GaJEmit99E+kJWApQHJYxJaXsx1SWbvUzRBiUeciIzQFa0rdDFFC+UrHTqmbUbYkYBFbllKKk0MxBiJpJlJZzowkOD0cJ56xSt00IeY5OTrA/vqFi6WJ7UPXNM6OJkvdjLIkQ0JiS7Jsh+ODcfY2VMxb+bZ3IsVAJEVHTUBWxd3miskvWU/nIiM0BKpK3QxRBjpqAySzFicGY+xpCJa6OWVFAhax5WQsmzMjCQ40LVyqekelD6UUXaNJHKXorAmgyzzCTac33osz2X2uyM+s0dCm/70cxey7niZSSaKZJL/ofgld0yYXqFPTi9Ut5Vx0hLcfumr9G7oCY+kxqr3VpW7GpuJ3m7TV+Dk+GGOvBC3TJGARW0rOdjgzkmB/4+IlnjVNo7M2gOMoukYTOAo6avzS47KJOI5TVgmzq3Fh4+qqrj589tgatWRtPTX0FLd8/xY++epP8sadbyx1czYVj2nQWuXn9HCcnXVSRA4kh0VsMSeH4uwr4o5E1zV21lWwqy5Az3iKk0NxtsDyWmKbKZeeovM9NvAYAPeeuLfELdmcfG6DKr+bnjHJaQEJWMQW0jWSYGddoOgVS2Gmx6Wjxs+JoThdIwniGQulFBnLJpLMkc7JTCMhinF8/DgAjw8+zlh6rMSt2ZyqAm4qPKYk4iIBi9gioukcHpeOxzRWdRzT0NnbEKS12k8qa9M9mmQknsVRirFEfqZR10iCM5NfXSMJukcTEsyIktIxyFnlN/vtdOQ017Vfh0Lxk7M/KXVzNq2qgJuw38Xp4eKqJm81ksMiNr1U1mYwkl7TjHpD16gLeqgLeqa3FZrD4TiKgWiarDW/fkLAY1Jb4V5Rr89WNbucva3sOf9WSs3ZNnu7gzNnVk8sGyvhuygvlb4AY+k4DRWVpW7KHAOJAd7Q+QYimQhfeeErvHX3W3GsCGPjv6AisJeKin2lbuKmEfa58Jg6L/ZH2d+48ISCrU4CFrFpTc300TVKOv1P1zWaKxcupx5N5+g+ryvXFc3SEJosBz91zplKQdAm/60Bhobud6G59LI7OcWzcY6NH6POV5efzVLk9OCpsva6pmNoBpqmYWgGuqbj1t3T/z5/H7Gwan+A0WSirAKWeDZOLBujMdDIDR038ImHP8Hv/vB3+cPdhxg4+7+pGHoZYeuVjLT/O03Nb6Gz8/9D16XC62K8LoN9DUFeGojRWRvA61pdj/JmIwGL2JQGImniGavsZ/aEvC5C55XZPjmRxqxder0YZTk4SQsnlp0OaAb74gQaA5M75P/j8hq4PAaGqaMb2oZc2B0c2oJt1Pnr1v21xNLCHh9d4yOlbsYcfYk+AFoqWriw9kJcuotPPvpJPpUd4DdDIaomXoeVjnO6WePb3Wep7/9jbm45wK7290hwughd1zjQFKJrJIHb1AveLG1FErCITadnLInfbbC7fnNO9UumlpdroJk6Rsg9Z5t3LEVlvX/6e6UUuYxNLmOTSVrYlkO8J0Zj+yLTujUwwh40c+WB3mhqlM5w54qfL9aWy3CRs3OlbsYcL46+iIZGZ7gTUze5ec/NNPgbeP+Pb8PKGnzCVcGj/ig/4xr+vwNv5Wcn/56PnR7lHxv78XqbS938stdRGyCesTg+GKO9xr/q/L3NQAIWsakMRNIEPCbVAffSO5epgG8Vf3bnzV7VNA2318TtnTmmM5FZtAdHOQo7kiEaiWBZ+WRhn9eLz+sDU8OocC8azCilSFmplb8HsebKsUfiyaEn2VO1h7AnPL3tyh1X8rqGPbw4fpKM7fBE5yDX8DAXNHyMRvctfPYZB7XE8KJSil986ySxsQytB6q44NXbdzmDCo/J3oYgxwdjdNQEcK/iJmQzkIBFbCrxjMXu8ObsWdkwS9SRSaVTxHNxKpurcLvzgV8ikWA0HqUmWI0dz8ECCcTT+TVAMOef/7goqXKrxHJ05CgX1108Z5tSiucTWZxUM/85HufqsTN8vfqt/PTB/0PSmeA3eQFd/+tFj5uMZnn6xz0AjA8ktnXAMmVvQ3BbJONKwCI2jZ6xJK3VW3O8NhaLkc1mATAMg8rKykX3ty2L6MgQkC9HH6qvR9cNomdjZGPZgs9TShGPx6mvr5+zPRAI4Pf7GR4envfYgrplRdlyU26XqaHkEM0Vc4d2TiYzuMixI1rNr0xcQPakh9/f+1369Ch11XFQSVzm/OHM/pMTfO/uo2TTFo6dD81Ctd45PYvbXb6nJc6+xq1byl9+22LTyNrOph+ntSyHTHamZksul2N8fJxQKEQwGJzeNjQ0RDAYxOc7L0BTMDHQD0BlQxOapqEch4nBfnTDxE65aHx5Y8HXj0ajVFUtPEFb05afsJu20svaT2xPOSdHJBOhxlszZ/sOr5u40chw5TAPj5ygJVrDxItvp/XaTl75ylfOO042bTHUHePs86OkEzmuevseXB4Dr9/Fz79xguGeGMcfG6DtYA3ewPaeYWToGm3VfrpHE7TXBErdnHWxtQe8xJbRH0nNTAXepJ5+sp+z3RO0tMzcQY6Pj1NfX4/XO/PeXC4X9fX1xGLz64wkomP4QiEqG5umgwtN16lq2oHb5yMVjS7aBr/fTzxeuPjUcgOWs6keuiJdy9pXbD9jqXxV2/MXPfQbOuHsSwx7YzQ01OLGZK9rx4LBCsAT3+/i23/zFE/98Cxur8Hhq1s4+Kpmdl5SBxrYOYcfffEFnnuwd93f02bgcxvomrZlC1lKD4vYFJJZm6bw5h4OCobc7Nw19wRuGIV7jHw+H8lkEr8/ny+ilMK2s3j8C989+YIhsulzi7bB5XKhlCKRSBAIzD3O+Pj4/B6dAnaGdtId7abR34jXtbkDSbH2RtL5KdYLTXvPOhYtbg/trhRupxVN8/KZH+YXb3QiWa70BXBpGrbl0PPiOFWNft7we4fxBlxos1ZV/40/fwUo+OrHHsbOyRDllNbqrbvKswQsouxNJLMEPVvzo7rYQovZbHbO8I1j5acwK6UW7AlxHBvSivSJ8YLHdBwL0ztObCTDSC43XfBNKYeKgB/HZRKbevpUATsNPDU1uDxVaJpGZmKMVGSAV1/wah489yBXt129gncutrLh5DAA9f75+VAKaPP5sPV8T18m2Ys/djv/5/n/xq19Hp5iGH/YjWHoGC6djsO1c6byT3G588G+pms4TrmlHJdWc6WPU8Nxdm2xVZ635lVAbBmJjMVEMkdH7dYck/V6vYyPj8/LK4nFYhiGga7PjNoaLp2q+kaGu89Q19aBNusxx7YZPttFsK0R755CiwhAenwIr1lHKFh4n/M5tkN2dIR49iQohStUidlgonDwuXxk7Awew7P0gcS2MZQcQtd0qjzzP2caGslkF2PB/6S2JkAs/CR7qs7w418z+Ne/A3T4nb+6atmvpekaSgKWOSo8Jmalj5NDMQxdp63aj6GXW1p28SRgEWVLKcW58dSWznqvqKggk8kwMjIy3WuilMLv908n4c6mGwbVbR2MD/SjaYCm5acxaxr1HTsZW6R3JX9sB/TiUtd0Q8dbX4+XmbvlditA19gzXFJ/CU8PPc3lTZcXdUyx9sppNutIaoRaby2GPn/I0+2pp67mlew6+Dai0WcY7fp3AMzUELpp4A0vUvRwAboELAvyugx21wdxHEXPeJKpH1Frla+sq4MvRgIWUbZOjyTYWbc1e1Zm83g8eDzL76HQdJ3q5kK1J5YouuU4aAtcRKYfV4q47eDRNdyLBDYeM0DOTuM23Nhqayb4bTZLlN/ZUEPJoYLLNuiaC6+3herqVxOPvzi93dr7KzznfpxXRfuKei1NAyUpLAXpujY9a0gpxZNnJ8hYNlfuqi1xy4onAYsoSyPxDGGfC9cmvRNYD3bWXsZY/eKPK8dG0+YHLEnb4Vw6i6lpBE2d8ZxDVil8us4Ob6GqwmV0Sy/KynBqePF1ppTiH37xezyWbQfezSt5iMtO/oCXmeeI5A7P2dW2LB779jdJxaI4jsPha2+grr2T4bMxnv9ZL8loVj6KyzAUSxNLW+yuryDs25xTwCVgEWUnZztMJHObdq2g9aJQmEuV9V8qnnGcOTMtAByl6E5lOFAxf4bQRM6iN51dMGiZPQRRKBFYbJxy+vEPJ4e5sPbCOdtydo7xzDiWbdF+soFMupb/+coIXYOf43/xYdpjfp5N7WdvzsS2HIzJMvNH//N+2g5dRPPeA9iWxb/8yUd4552f5Xt3PwtAuN7Pjr2VG/0WN43hWIZoOkdthYf6us09o08CFlFWlFKcHIqzv8zyVtJZC0spnMmuZ6VsDM1C4QAK5Uz+a7JvWgEobXIWkELhkEpNkImCrrvR0LEd0Bxjct/8e1eOmu7aVyg8s4Zl7JxNZiJDajQFSpHKpEkm82v6TKWyZCdy1DB36vRsSjlo+tw/+7PpLPsCC5/IKl0mg9nFi8TtrtzNsbFj7K/Zv+h+5So3OIRKJad/iMpRuFtb0FyluQvtjYzRNTGIoeV/987kB0LXtOl/L8TnKo/EZ6UUZ2NnubHzxultOTvHpV95OblEB62Ry7gqshsA86IUGg4aDr/3oIuP5vLvIRnNEqzOfyZdXi/x8Xxdl1w6jTdQgWM5xMczvP53DrLvFYULJW5nw7EMkVSOugrPlpktJAGLKBuJjMW58RS76yvK7m7928/0Tff46JpG/+hpDrXUzlSH1TSm/6dNThbWJmcFTz7W3laFUha5XBJQDPWMUl27Zzp5VgN0Q5sOPkZeGGfHBdXTt866qRNsCqAcRTwRRylFdeXcWRhWlcXo6Cg1NXMrjE5Rtj0v6VYDbAWFJhEYBX8V+Qfq/HWcnDi5xE+wPGVOn8Gsq8VomEkoVkqRPXUKs7EJo2LjcqhGEnGeG+yiviLMq9oPbNjrrrX+RD+JXILdlbunt8VzcRQOZuA01tnbprf/1VgXo+atvL3jZv7+4iru+x8PAxAbS08HLAdf8zqeP/JjfvpP/wfD7eam3/8I2uSHcuRcHG9glMadITz+tQswu0cTtFX7y+48tBwj8QwTyRy1Fe4t10stAYsoCyPxDKmsXbYzglqq/FzaPtNz4dODtNa1r+qY3miGUPPCJ5TB50bY8cpGzPPqz0ydkpNaktrahZPmJiYmyOVyuFwurGgWMzR7OEfNmSoN0OZ183w8xQUVvnkn6OFsjuAixe02M2t0FLO6CuO82ViapuHZvZvM6TMYFZ0b0pYfn3yG2kAFV++8cOmdy9zx8eMA7K3aO73Nbcx8Bv/XOy4h+fXnUCh+xfgrDMPP1Tv/x5xjdB8doXl3JZD/fVx4zXVzHldK4fGbPP2jszz9o7Nccn0bV75tN6ullOLEUJzGsJczIwkcBW3V/k2xCvJoPMN4MkdNYOsFKlMkYBElF0nlSOdsWqvLdwXgxQq8rbXR4+NUtgbnBSuzLXbnZ0YdxrsGCFVUkO4bwdgJU70huejEgsfaF/BxMpnBmOodAhwUIdOgwbPwnevmu/ecy4nHcbcvEnRu4NSTgMvLxU27Nuz11tPx8eME3UEa/A0LPv7MT39MheGgozH66G/T8sr/x8DAfTQ2vnl6n6d+fJbWA9W07F94eFPTNH77jivJpizu++zTa1LpdiKZZTiWYXddBbquEfK6pksrZCyHprCXQBkWsBxLZBlLZKnewoHKlPL76YttJZ6xGIlnyn6MdT17hpPRCJlkAoBYX5xAVRWe8OL5CLZduOJtZHCcHZflL3457wjBlj1LtsGla+wpkMdSiEJNt8FtuEnlUvhcm2f5BLOujuy5Xtwt86eIK8cpryzWTeT4+HH2Ve0rGFSbKQ+X2Pm8kye0cyiV4fkXPkxj45vRTQ3HUrjcBl1HRwsGLAAen8kT3+tiYjBJ28HC+y1FKcWp4ThBr4s9DfN726ZupHonUvRHUnTWVpRFEbbxRJbRbRKoTJGARZTMufEkwNoHK44DkR5wLPBVgX/lJ7NC1JLTcZZnpKebQGUVVY3NxAeThGoqMIM5xvt7qWoqVGsFamtrGR4exuPxEA6HAUgmk8RiMSq8G5N34TVDJLPjBDzVXFBzAc+PPs+lDZduyGuvBd3vR4vHyZw+g7u9DW1y6MsaGcEaG8OzZ+lAb62s1eepHPQn+tkZ3knq6HNEvv3tfH7Wvpneo2R6lGd1neDOLMnoOP+zJx8Q1Hz9at7g+iABqwp/0INjL/0zeenhfurbg+xdYeLtcCzDRDLLzrqlg5AdlT6UUpwZyd9c1AY9hLwbn5g9FahU+V3bJlCZUlTAcvfdd3P33XfT1dUFwAUXXMCf/dmfcdNNNzE2Nsaf//mf88Mf/pCzZ89SV1fHW9/6Vj7xiU9Mn1AXUigK/9SnPsVHP/rRYponNpEzIwmqA+61rwcweir/38o2MFyQGIXhYxCoW5fApRg9J5/H5Z7pOUlGJmg/eDGGmf8ZZCJpUGCnNbJpxdlzJwmEqpk9+OIOugg2V6DrOvX19WSzWUZHR4F8mf9wzINr50zAYvqD/PKp56iqK1RLpXiW4xDweNhRs5uusafZW/9KvKaXrJ1ds9fYKK76elStQ+7cuXyms1IYVVV49+5d+sliQVk7i882GP/av9L08Y+jmSb9X/w/01ebq3MH6NfH0SNVXFSxgxrDYdTW+fX9v865p0cIpKpAU9j20sM8jqPY9bJ6GjqKq46bsx3OjCSorfDM61VZjKZp7Jy8weqdSBFLWzSHvRuSnDuRzDISz1K5DQOVKUUFLC0tLXzyk59kz549KKW45557eMtb3sJTTz2FUoq+vj7uuusuDh48SHd3N7fddht9fX1885vfLHjM/v7+Od9///vf59Zbb+VXf/VXV/aOxKbgKLX2wcrYaQg2gXtWLkygJv81chLcATBXNvVzLVJYXC43jW0ziYHj7r7pYAWgZu/sgKqa0XNnqWmZG2SNHhub873b7Z4zIyjljGMEZo7pq26kdsQFKdi5swpjjQrxRZJRjvWeZjQ9zN7569ttKpqu425rK20bNn1G0IysncWlu4Dc9DaVyXDF8XwAMmAM0uI0wTCcvuBr3JDK0ftzF1d3HefnxkEyAGg41vyAJZ3Ice9fP0lVgx9/2EMuZRcdLPROpMjkbPascjbijkofiYxF92gSBWQth111gTUvex9J5hhJ5AtpbtdAZUpRAcub3vSmOd/fcccd3H333Tz88MPceuutfOtb35p+bNeuXdxxxx28853vxLIsTHPhl2psnNuV9+1vf5trrrmGnTt3FtM0sckY63FHotTcYGW2ml35npb6VdQKiQ9DJprvuTkvgnGU4tR5Sas2inq3i5C58Cwb5TiTpfLnn+BymTQuT3E5JdneGGbd/BySPXtrsHIOzz4zwKHDjZhrMOMh7A9xqP0gv+jWyNk2LsPAkfroK7aVhoTi2TiBiiqqfuOtDH7yr1C5HKmXnmfsUP5v49gvv8szN17Luysf4ofNt1H5ua/xVFMPv9H8Q258LkMLO1FK8dIvB6hs8NPz4hjVTRUYpkY6kWOsL8FYX4KqRj87X1ZH50XLKzGfyFj0TaRoqvRRUbk2uVYBjzmdiKuUoms0iVKKztrAqntdIskcw5MVv8s9x2+jrDiHxbZtvvGNb5BIJLjiiisW3CcSiRAKhQoGK+cbHBzku9/9Lvfcc89KmyW2s8W6QXIp8Kzwjz4+hC9yGloPQMUusDK4zz0AFV6o6kApxQsFpgWfTWWwlaLKNf9voKqxmZGebiobm+YEJ+lEnMT4GDUtxd31W2Np/DsW7t42XTp79tbw8CPnuOpV5x1XqXzOjz15R+yugODCMzzOd9mOPTx67gSvat/PgZoDPD30NBfXX1xUu8XWEs1GCblD+A5egO/CC0g98wz68f0cS94BwC2PjxF65kv8y62f4YVchpvSp/lci4u3Hf1jqtJVOPYEpjs/rPnwv58GIDKUwnTnA/+algre9IGLCFQuv7e0aySBaWhFDf8US9M0OmsDWLbDqeE4VX43NRXF9+hGUjmGYxlCPnPb96icr+iA5ejRo1xxxRWk02kqKiq49957OXjw4Lz9RkZG+MQnPsF73/veZR/7nnvuIRgM8ra3vW3R/TKZDJlMZvr7aDS6/DcgSi6azuF3r0NtD381jHdD1XlTVe1cfriocQU1LtJRsDKkwrvAO5mLZXrIVraDrxomejjrqWdfYH6wAtDm83AqmabKZc5bB0jTderaO4mODBEfG52utOr2+YsOVgC8e6pIvTiK70AN8Xh8zt+IUopgMEhjw3kJuckxiA9BdefMcFk6AoMv5LctMevHZZo4k+V/q73VHB05SspK4TM3z2whsXYydoa0nSbkmckp8R46xOh3/4NfP2mjKei1Avy3mneTvn8QE4eB5mt4z/0P4ngHcZzTaHqAid4nuOUvP0ywuvgF+nrGktjOTJ+VoxStVRtXS8U0dHbXBxmKpTk9HM9v03XCPhfhRYrbTQcqXglUCik6YNm3bx9PP/00kUiEb37zm7zrXe/igQcemBO0RKNR3vjGN3Lw4EE+9rGPLfvY//f//l9+67d+C6938a7wO++8k49//OPFNl2UiaFomt3163Cn468G3YSRE6Cdd3JquGBlx0wMQ80utJHh+Y95QxDtw3bnpwUXYk2eOWORPga6F9hvKtBRCjSNTDZJLDo602M06/FsvB93b+Hej0w6SnI0n4B7frXb8fFxkqnUzAal8sHKecNkT42NkbEMzJcewAovXTjtXHQYx3HQdZ1XN7+a+7vu54aOG+YVqBOFuQ0XD589Nj00NPtXv8DHYJJiKiFb0+B05ASdoT1znrNWZl5pYRoaCsVgMr/ScqN/Zqhf03WqPvphvv2lrwGQ6v0d6owMB+o0ftyt+IX3Ip656SFu/c9xGq67hssOHub7n3uQe//qU9zyV58quq22o+ioLf0q7/VBL/XB/M2Co/LVcxcKWKLpHENRCVSWo+iAxe12s3t3PnHw0ksv5bHHHuOzn/0sn//85wGIxWLceOONBINB7r33XlzLXI/jZz/7GceOHePrX//6kvvefvvtfOQjH5n+PhqN0traWuxbESUwnsgS9q3djJV5vKH811rRNHDswo/rJs0eF6eTGXb653f/Ri2bwGQSXl2bm+rqlU+VVUoxFhwiWL2v4D7uyglG+3uo2X1o3mNVVVWcPjMraTc5tuDQTyqX4cr2/TAM1C38WrnBIZxkAk3XeVllB9bZs2CauFtaeH376/lh9w+5vv16CVqW6eUtqy8ap/dFeEVzaddz+kVv/vO1Izh/Sn7Kkw95mp1eAjo0ZzJAGzOhkEF6+Hncnnrc7qP4wtdsTKPXmaZpGBpUeEyGYxnqgvnzRCydYzCaISiByrKtug6L4zjTXc/RaJQbbrgBj8fDfffdt2RPyWxf/OIXufTSS7nooouW3Nfj8eDxlMdCX6I4I/HMuo4jr7mqThh4Fk01zX9s5CQEG/AaOlUugxOJNEHToMFtErcdBrM53JpGm6/wZ9VWiu5Uds7da7vPjb5gwp5CP7/n6DxZx8BlWAUfn7N4njcME935WjVz2jQZoBV4rey5XvSAH0/D3N4XJ5Uic+oUnl27uKb1Gn589se8oukVhD2FyxqIreVc/Bw6esEqtwBpzY/lquDVzrfoMI8wbsT4J8DwvRpXqJ9MMs6OAzfh2Cs7x1uOU5arh9eHvAzF0rzYH8Vl6FR4JFApVlEBy+23385NN91EW1sbsViMr371qxw5coT777+faDTK9ddfTzKZ5Ctf+QrRaHQ6t6Surg5jsijT/v37ufPOO7n55punjxuNRvnGN77BX//1X6/hWxPl5sRgjPaa0nfVFkXToPEwnqceg9HY9Gb3RDfsvWp6VlKVy6TKZZKwbbrTWQKGzm7/3ID9/JkgYzmL0azFLr9nOkBxlOJkMkOd21wwUXf6WEqRUwrX1OKLkyoqKhgfcMjF47gqljgZGmZ+xlNscE5Pi6mZMN4F3sqFXzuXxayafwet+3zoXi9OJoPH4+H6jut5bOAxUlaKlze8HL+rfJdeEGvjuZHn2FHRhqkX/uz6nThvy/2QRzwH+UfrIJo+RAWfQdM0VLyF2PgOoiP9hOtW1jvXURPg1HB8fYadV6k+6KU24EEvg0q5m1FRAcvQ0BC33HIL/f39hMNhDh8+zP333891113HkSNHeOSRRwCmh4ymnDlzho6ODgCOHTtGJBKZ8/jXvvY1lFL8xm/8xireiihnPWNJGsLeTbGI2DyaRia0E2pmEgCzKWvBKdQBwyDgW15C8UjWYu955fB1TWNvwMvxRHqBgEUjYsFoMo2OhlvXyDkKG0W1y6TaZaJpGnUtBzl36gl27L0ctzs//JZIJIjH41QGz+vtqGzLDw2NnJxOkjCiZ6HpynzdmgV/HIucbF0uVDoNkz2glzVehuM4PD74ODknh0t3cUn9JbiMja8QKtbfyYmTtFQsvihov6uVhzI72Zs9hY+dtOaSfGDMZrgBUl0GT/Z0oxs6e3eurGfONHSMMh6KlGBl5YoKWL74xS8WfOzqq69e1gJxC+3z3ve+t6jZRGJzGYym8bj0kpSxXitrXSdDKYV7kRPXQq8Wt2xitsYh//yh1nPpLDoWlS4Tt65RW1NHOp0mFsv3Cvl8PhoaGojHRucf2F89pwqwk7AKBisAyrJQtj1dyn42e3wC1765VWJ1XefypssByFgZnhx6krH0GDd13lTwNcTmo5TidOQ0N7QuPqw/kbI4wiUkNB//tPtnfPeMw9hF/8Ktr7tk1W2Ij49hmCa6tjVXGN/uZC0hsW6SWYu+iTSVfhe1K6hHUE7WetaFpuV7RwpZ6P5wKJujxbPwc1q8bo4n0lS6TDAN7EyayqaVJR8v9V7du3aRPX0adB1XUxOaYZDr70dls7g7OxZ9rsf08IqmV/Bw38MrapsobKVBtZPJYA0Ogqblf5/LrJt1vsHkIIlcgkZ/4TWwAC7cEeK53iiPqf2842Q+SfiP9JUP38TG0tz/j08zeOI7uDyVKGxs26b1gjfwxt+7SHo0thAJWMS66BpJ4HHpklS2iEqXwelkhk6fe3qYRSnF6VRm0fyVQqbOy4bhQncVVyW3GJqm4dm1C6UU1uAgyrJxNTev+EIn1kax5f2dTIZcTw+634+rtRUch1xfHyqTwb1z54IVmBdzOpIv8rYj0FJwn49d8TFe3XgZj54ZI5bJ0Rjy4TJ0Lm2fSfzue/4hnFM/obqpE/8l/2XR5TRs2+HL/+MX2NnToHWAtRNfyE166N/oevp5cukL8CxS+0RsLnKGEWuudyJFld+9aJGkzW45w5/znzS3dH2d20XYdDidyqChkYwdxbKTNLkNNB1GmapvkeeyHfr0eqrmHThfUbfePfPz1jRQjkJbx7tLTdNwNa5sldzpmUhiWY4OHyWZS87bPtWroqHRHGwu6pjZ7u65izwaBu7WVpRSZE+eLHq16jORM7h1N9XeuoL7BFwB6kNefuWiAm098SP+5av/j6/b13BxYJR/HPhjeNPfLvnautmIph7CMDzERzM4tonhbSi7mUJidSRgEWtqIpmforuVgxVgXvCxUm5dZ9dkTspoOkNNw2sK7ltDvq7LiUR6zlpMDopalzlnzSKFWveAZbkSls1Q1iJkGtS486ccU5NTTzESuQSvbH7lmh3PSaUwwpULPqZpGqwgaXU4OUydv27J/JGHvnmCoe4Y7YdqUE7+c7rz4nqqmwPQ9zQDnW9l5CT8OBGG4/970WMZhs7vf+51AGSSr+Ps0aexbZ24cQOdLVW4ffI520rktynWTDxjMZ7M0VkGVSbXm6avIKlviRoqyxEyjYKLKc4WaNhJtOcolR1L1zU631rdlI5mLcZyFkHToMPnJmLZnEym8ZbxDI7tQnO5UNlM4R1W8DuKZWOE3KElPz9P/7gHgNHeOJqWX9AwOpLmdbccYGLvJfz+8b/ghtpadgSDcPXfLPv1PX4/e15xJQB9EylilkN8NAEsXDk47HNRFVjHIpZizUnAItZEMmsxGE1v2VVFh5LD/LJ7Yvr7eHIM19Av5+yzeA6B4sz487RahffJZYcYt35Z8PFiRcYm6MjMrQBtJHI8f2yBmUKzZNIWL9qn5m6cNQRmjVmEqkw0o/B7GUudIhoM0exWxIH4rOLuQzb0R7ug+RXFvJ1trdj8lCWPZ5pg29jxBEbF3BsMa3wcfQWFOacClqXsuayBZCTD9e/ZBUrxwNd6iI2lAegd+z4Du3ow1Fn2X/0cGCur3dO8jNWYu0YSErBsMhKwiFVL52x6x1Obq4JtkRrDLq5onz2mX3yJ/WFLY1/92nXrL+VkphtCGjXhmayXmkX2n7H4XhNMEGypwHAXPn1EurK8puVAwcfHcqUfqtpM1npaPYC7o4Ncfz/2rHWylFIYFRW4movLhwGI5qIE3cElgyulHEa6v8czP9wFGoz1nCVn5XtGzloh7uct+LUk2Z+/jhte/SC6LkGFyJOARaxK1nLoHk2yr3HrBiullBsczBdiA8y6OnT/8u84d7e289KZU+RyNo21xa96W4hm6ChLwSLXEU3lq/YuvMTAzIwmsTz2YutZrYKraYElJ1Yolo1RH65fclp8OtaH29fAFb+WLxSafPzLNDsZvv/lL/P1FoNfdbrxtr6Bf+i9gNfZGTzrFLDkbIfu0QRKgcel0xT2oZTi3HgKy1FU+V1U+iVYKicSsIgVU0pxcijOweY1XGywTEUzqaV3WkPW+Dj22Biu5mb0hnzZ/NzAALneXty7dy979sP+zl2cOneWwdFRGmqW17+yFE3XcM6rIZPJZIjH47hcLoLBIG0eF4+dfokDnrlLBygUZ7MOVdEUyHqly2asJGdqg0UzUULu0JK9Qd5APQPRB+h65knQNNxpjaDHw+Mt/8mljHPg7M0ED9Uz0JtiJGexY53y92f3CI8nspwYjGHoGq3VflyGTtdIQgKWMiMBi1ixU8MJ9m+TnpWQZ+kx8bWiLAt7fALPrrkr+LoaG1F1dWRPn5732GIaqmrpHx1au4DF0KYnSSWTSRKJBF6vl+rqanK5HKOjo+jeai5trOJMKoOh5QcJFPkZTe1uF+PnNnchwY22Fjks6UwGpRS+IhalLUZXtIv2aDsNnpfwDib5Rd8v+Pyzn+eOq+6YXrSz//QE4UgdlS2/gm3lULYixg4UcBkP83XeybHaZrJnJng9/0mD+4YVtWUomiaZnd8rpcj3CntMnbRl4zUNsraDoWvouobt5HtYdG3hatOitCRgESs29Ue+HaSs3Ia9Vq63t2DF2IXK4S8mloxz+lwPF+0rnE9SSDaeJd4bB8Ad9FDRHCATy5AaSuKr8mJZFslkkrq6mbobbreb2tpaMpkMscgEe6oWqhoD49vjY7NmFuu1SKSS9A4PkLXnfkZnz4jRNHCZbjQ00rk0UznQu3e043OvPhgfS48B8MC5B3jg3ANzHvuTn//J9L9P3h+lORrBMHX6z+wgm7QwnDS4dA7v+BSunj9EVfwTWhIMI7Di/JVUzqZjG8xW3G4kYBFiGXzm6vull3uXrIdC2BMTmAUu9sudHu0oh+NdXVx68MLlNnGOeG+c6n35NYaSoylGXxjF8BjUX1wPwOjoKLUFcmM8Hs+8RU63qideOorH4waVDyzqQ3XU11SvumhZNBGjd3QQ27YZT8R4PnN8wcDF5/HQ1rgDr7u4XivHdjjVd5ZMLj+9WTNgV3M73hVUSQ64Anzk0o9Q46shkXTzita9oIHf9OM23DjK4SsffYyr33oBxx4ZYHwgydnnRtENnX3VHlyVHnxnDrP7Z/8b/5XVhF7fjmH40XXpiRMzJGARK2LZzpziZWLtmFVVZE6fRnO55005zfb0YFRVLus4zx5/icO796+qLY7joOs6/hof/pq5d+KGYWBZFmaBkvz6Nqm34vW6uaBjH5DP6xqaGOa5M8fyAao2WddGzc3j0dBmqiVrkz0ok70eU4FthTfAzqY2PC43F7JvzdutGzp7Wjumv7csm9N9Z8la2fzjpsbO5ja85tIBjMfw8DsX/g4A3z/+BDsrd87bx2V7MN0G1U0B3F6Tm//bywAY/j/Pole4yPYlMGwftTdduuqCh2u99pcoDxKwiBUZjGWoD22fu5+1roOxFM/OneQGBrCGh6bXdFG2g6uxYVkzhbr7z9Fc14BrkanHhYw8O4zuMXCyi1fzrayspL+/n4aGhnnBycTEBP4iZjRtFZqm0VBVT0NVfambUjTTNNjb1jn9fS5rcfpcDzkni0IR8PnY2dS+5HHCngJDMWrhooTWaBqrOwK2wgh5yqI6syhPErCIFUllbbyV5T9zYa2sRx2Mpax0nR6AaCJBe1PhRegWU9EWJDmUovbQ0lOhGxsbGR0dnTsTSCkqKirwLpHcqZTaEmu9bHQwu1FcbpN9HTMBTDQe5+jpl/B7fexqXjpwGetLUNXkR9NmepM0TWP2j8tKZ3ko8RxKUzimQ1NzK8VXgBHbhQQsomij8QxhX3msFTQQjfDswFkON7bSGKpct9fZTD3MZwf7qCmwTsxyeCvzgcbI86Nk3BrNu6sKBhaaphXMY1mMrhtYtoVridwgpRS2o7AchWU75GybtJUlbeVIWRnSVo5MLseTfT1c2b4TRznYypkz7dqZvFieXxPGmTVuMPWYc95YwtRTzh9imL3o4ODIGAc7lv3WN61QRQV73J08efwokVgM3TbxeF04lsPgi4n87DEFfZFRBn6cpP+xCSrqvDRdWIPj5Hvr+gbjRCJZ0sl8xeX+XzxJq1PDDqeaF/eO89D4swz/3I3H78NRDq3+TtwryGOJRDO0Vfu2zbDkdiEBiyhKMpfksXNnCHs9nBiFnGPhWkaNiNkzFqa+h8W3Fbr5Pv/icf3eQxzt7+H02PCc7Qs9f7lj21N5BlP6k12spLrtbH6Xn0f6Hlmyt+b8O/b8is1qyX9D/gKfGLZ43cWvmnMMx7YZH+hD13UeerSf+rp8kSxvRSWmt8AsEROaqn10nxwreOFeSqHfQS7n4TH7BGdGM3hNk+bKub0xs9+bqesYuoahgWnoeE0XPpeHsK8Cn8uD2zC5rLWTrGOho2HqBrqmbdjF6mi2ekNepxx43R6uvPDl5DIWwz1xmndW8uQPu3nx+z1z9oswAUB8OM2pB/tQkwFkU30AohYTGZs97WG8Zxp54tQjhB0/jw7/glP6EF89+c/TvTC/ffC3+aPL/qjodk4MJSVY2YIkYBFFGUgOcOP+C0rdjHkONa1vFbKH+xZff2c5DtUdWoOWLO6Fkye56ODcn4VyHEZ6uqlr70TTNDr3e7lgX74my8TgAN4KD95A4TWgqmtWNu01k7P5j2f6aQx70LSZMExpCuUy0ax6rmzz0rlG60+ZRU75FiunGxqOne81iY3kKzG/5zOvxnQZaIaGpjGnV+7oA+d48F+Pc+rJYSqqPAyeifKFP3iQSkPj4lArp41Bqlw1fOQ330nUifK+H70Pt+7m+vbrS/L+RHmSgEUUxVhi6fitajPkKfQNDFER9M+b3joxNEBdW8eCwzqVDY2M9fUuGrCs1LnxFK/YVU1r1dZOvt0CaThF0w0d5UDPS2NMDCVp2h3G4y88vGeY+d6OjsM1dByupXlPJYZLx55IU/Hjsxy223GqFBc2XkhfvA+Av3/d33Nx/cUb8XbEJiEBiyiK2qbzBTdDwDIcHeOivQtMY1ZqeqbRQpSz+Gyg1Sj/n5pYCU3TaD2QHwp7/Htd6Ius3J3fP/9f020QqvERuiLfazfcE2P0/m4qDI3GZ3sZ+Mu/JJYco6pR4XcVDnT773wEJ2UTen0bTtbGiudwkjlU0kLzGth1fqzLGzGr1qeqrygNCVjEsp2LnaPWt3aL6Im1ZdsLL5AXqKomMjRAuH7+rKN0PI7btz7LDoR8LoZjGShQ/24rcxwH2wHbcXBshePkk4cdR6HU5HYHnMltPp9JdXhzXlyTkQyBsIeeF/PVbr0VLupaCyzZcd4Nj2MpJmxFhaExeEETL3/Pu/jlU//CH334ezS/u/B8ITuSrxUT+d4ZHBTPBs+RNi2UobGDatqeCzHw0x6C17QSvqFjTd6nKD0JWMSyDCQG8Jk+KtxrP3SwGTisXy/EWilU6dTt9WFls4z29uDxB1BKkU7ESUYmMN1uQrUL1AwZ74LZKwQbLqhsK6o9Ia/J6eF4Uc/ZjFobK3j22Mi87Yauo+v5RGFd19A1DU3PbzeMfK+drmu4XDonuyNcfnjlAUsyGiGTiE9nt7t9fgKVGxMpKgW1bcHpHpepwGUubXrf2TwBk+RkQq4nml/r6NEH/pXDB1uo9xeuZeN/eQPJxwcBOKsPs/vmS9h/ML/8xBe/+EUu+I1XEvv7F4j9tIfAKxoxKzdnMCjmkoBFLGkgMYCu6dT41mbxvM1oMwwJLZZL4Q+F8YfCZNMpkmNncJp3UN28QJ0Wx4Gh56F2L5izAqBcCgaOQsOFy07aeLRrjLbqrZ2/AhAOeji8b3VFFPuGEit+7ui5s/jDlVQ17ZjelorHGD13lpqW4oLMYtk5h9hImsq6xXvpCq0m4fW7MCc/Tr6JJC/92UdJJM7Q+NG/m7NfJBMh5A5N13QJvrWT0PXtpJ8fJXBflHNneti9dw+piTjmhMP43x/FJJ9vpy+SWyM2FwlYREEpK0VfvI8abw2V3spSN0csw1Qp/ULcXh+Bmgb8ofDCO4ydhvoL4PxjuHxQdyD/eM3iK0X3jCZ5cSDKFbtqCHrlYrGeIkMDhOobcJ3Xu+arCOLyeJkYHKCyYeUFCJcy0hvHcRQ1Lctbtf38DDi338Q1GQCPd9Tw04Nxnj/byLv8lfzFw3/BL/t+STQbZSIzAYCpmVjKAuBVO17F37T8JXUqhP3zCb7X9S0YyvBKZyeBvbV4OkIEr26VyrlbiAQsYp7R1CixbAyP4WFX5eIXJ1E+Whobeeb4S1y0d//Ka1Bo2vxgZYphgmMVfKrjODx4YoTqgJvrL1i/i6SY4TjOvGBliulyYefWd5Xx008P4/Gb1LfPBCwL9qZMxQznjazquoY/YELOIefk+M7p7/DOA+/k75/6e54beY6L6y/mrY1v5YnBJ3h548sJuoIYusGPu3/Mudg5rA4XzqEQjUehsacSd2uQmncdxKhY2SrPorxJwCKm9cZ7ydpZqr3VdIQ7St0cUaSgv4KDO3fz4plTAFywawWF7gw3ZOLgWSBXKR0Bz8J30l0jcY4Pxnn1nlp8K1i/aLtb6RIFuq5jZbOY7vkXaCuXw3Ctbw9X17MjdF5cNz1tuZDpGjxKceqJR+g79iJVzS0cuOpqgpMPnpo4RdKV5G173sajA49yQ8cN3HHVHQD8Lr8753h98T4e6nuIK79+JaZm8oWa/x872/YSfG2LBCtbmJxZBH3xPtJ2muZA87JWZhXly+N2c8GuPUTiMY51n2Ffe+fST5qtshVGT+WDk/BMTgQTPWBn5w0HOY7DkePDNIa80qtSAuH6xnwOS2UVvoqZYDIdjxMfH6W2dek1f1YqGc0y1pegcWeIc8fGUJO9J97A/MvKVA7Y2eeOsPPiFl75a7/B4KkTHPnyF9hj5QsqOsrhVTteheVYPDfyHK9peU3B177l4C3sq97HYGKQTz/+aZJvrkWvaiUBMJQkHc9Rs6MCl2d71o3aqiRg2cYm0hOMpcdoqmjCZ67P1NatYq0WP7RGRrBjsZm6KJPTJlzt7Wu6EGC4IshYbIKhsVHqq4tMlq7Zle9lGT01sy3YBO65CbRdIwmODcZ4jfSqlFRNSxvJyATj/b1zZgmtZ7AC0Ht8HICW/dW07FtieYLJj3YqOsLOl92My+2hed8BfvAPf8Pu8GFwFEpT/NreX+OvHvsran21/NaB3yp4uEpvJTd03MCZyBk+/finCVS6qayf+XzGzDS65K5sOXKW2aZ6oj14TA87K3eWuimbwlrMEsoNDqKZJp7Oub0eKpcjc/wE3n17V/0as3U2tfLMsReLD1ggPyS00LAQ+dohDxwfpjbo5gbpVSkL/nAl/lUseFmMyHCS2FiaF3/RT0W1B9O1jHypyT+ftouu5cF/+RKmy00um+FNH76d3P/pB+B53yku5wYePPcgn7n6M9M3UU4mQ/yBB1CpFIFXvxqzeiY40icTZmYXtExMZMilbfQqCVi2GglYtplELkF/vJ8dwR3Sq1KEtehhcZLJecEKgOZy4WpswBobm3MyXgumubZ/4ieHYpwZTnCV9Kqsqc1UQTo6kqb1QDVH/uUYHYdq6byobtnP9forufx3PzBn2znyAcvVV93Ipx//NK9seiWvb3s9AMq2GfizPyf8ljejNzUxdNdfU/eB38fVnC8qN73o56y/z1zWpro5sKr3KMqTLGe5jYymRpnITLC7arcEK0Vaix6WxYZ8NK8XJ7HyWhwFj7tG9WMsy+FHLwxg2YrrLmgs32BFKcgmil9WWiybUopcxiYylKKhM7Ss5yz22T/REKCnyoutOfTH+7n98tun97cnJnC1tBC48kp8hw7hv/wyxv/1a/OOO5YeKxj0JZNJksnkct+eKGNletYR6yGSjdAZKjIJU6wpJ5NB98yfhprt6sazdwWzepawFmkxL/RH6J9I8bp99RhGmd7jJEYhNZafU+vyQ2wgX6k3UAv+te212spGe+Mko9lF90mMZ8im89PbPb6lLyGxsRES4/nqt8lIhLG+/GeoqqkZTdOYcCBXl+QLR7/Abx34rTnD1GZNDZrLxeCnPo3u9WCNjdHw3//79ONTN15/+MAf8levuJNXD18CHoPYYAKX3+Q/n/wZbpcbTUE6m+baS1+LBribK6Sg3CYkAcs20h5s5+TESfZUrf2FUSzN3dFBtqsLpRRmbS2610tucBCVyeBua13TpFuA/uEhqleR15DKWjx4YoTd9RVce6CMc1VSE5BLQu0Cn+vxbtAN8BYolCfmiI9naL9w6Zwn23ZAg1Rs8Tov0eEhXD4fFdU1QD++YJjq5h3YlsVoTze1bR1k0jm+av4dAVeA2y66bd4xam97H04qhcrlMEJze3RqfbV8/Ve+zju+8w4GHj1F5IWZ3/OIFsWrp9hn55coeNTs5+FnfsQepwnNa7DjY1cu4yciyokELNuIoRvsqNhBX7yP5orCC4uJ9ePu6EAphT02hhWN4mpsRFvjPJMpY9HIymqxAM+em2AskeW6A/UrL0K3URIjULt74ceq2mH4WNkHLKUewBofSBAZTlHduLzcD8PQ8QXdJCKZRfezcjlCdfVAau7zTRN/uJJ0Is6/Vv0tZ9Rx/vLSvyy4Vpnu80GBRToP1hzE0Az8F9URbGxF9xgkxzM0jIV47swvCfkqcDUGyMQ0DrZciJGG8I0dy3qforxIwCLEBtM0DbNm/ddlsqyFV29eTDSV4+HToxxoCnK4pXLtG1UKhRayKSOlms8SGU4xPpAgXOej41CRK7ErtWTZ+/M7DWcnxxouF9l0ilf1v5XTu59jIjPBifETOMpBofL/Vfn/2sqe3ja13VY2SilSVgpb2ehek/D1HQDYQ0kq6/28PXuA06dPo5TiV3ddiXuBAnti85CAZRsZS48xkZ6QqczbQE//AOFwcStrP9k9Tipnc93BhjUfnlpXwQYYOwPVC+RnjZ7K15ARc8TG0oz2xqmo9hQfqADZtEU6nsMfWjwAUAqs7KycmFldSdHhIeraOwlHGtip7+NTj32q6HZMqfJU8aodr5q33e12s3///hUfV5QXCVi2gZyTozvSTZW3SoKVbaB3aACLHJ2NrcvafyKZ5bEzY1zYEqYpvAlnj3mC+QTbkRNze1McOx/MFKgnsx0lohmGu2P4QysLVKb0n4ygFDTvrlx0v+rmHYz395KYiAL54GW8vxfHtqne0TK5TwVveOF9XP7hWjQ0DM1A13Q0Tcv/l/x/Dc2Y3qaTf3xqW9gTlpmP24AELFuY7dh0x7oxNINdlbs2112zWLHxaJQLdy+vCN0TXWNkLIfrNnsBOF9l/muTqq/x89yJ0YKPD42led0rdhR8fCmpRJbB01G8Fa5VBSpTel4cwx92E65fOkioatrBWL8L6MexbCobm+eciy64qpmffiXKhZWHcbmllL4oTAKWLWgoOUQyl0TXdDpCHdPVIMX24DjOkvuMJTI80TXO4dYwDSG5My21xlo/jbX+go8/e2xkRcfNpi36Tkzg9pprEqhAvtLxiccH2fWy+uXfBE3u5vJ5p5+jlEI5iqrG/Pse6YnTtGvtkqOVUnKTtsVIwLKFDCYGSeQSNAQaqPfXl7o5okQ6drRwqucsu1rbFnz8WH+UmMnm71URBVk5m3MvjWO6ddovrFnTC/fAqQmSkSx7Xt5Q9HMf+fYZHv2PMzi2wrHnzo3qOzG+ZgGL1+8ik7TwBqTWylYiAcsmp5TiXPwcOSdHna+OhkDxJxGxtfQNDdJUNz9gnYhmONUV4XUva6IyJKtybybLjTccy+HcsXHQoO2C6nWZkn7yiWEClR4al1nldrZLb2zH7TPRDW3Wl46mQ+uBtSvw5wmYRIZTErBsMRKwbFLJXJLB5CAALRUtuAz5wxR5OdsmXBGc/j6dtTl2chSf18Wlh6XnbTNazkoDE4NJIsNJWvdXo5vrMwyslOL008PselndklOaZ5taIuLQ1S14K9b/XCVDQVuTBCybjKMcuiJd+EwfnWEpsy/mq/D7ODvQR11VPcdPjWEaBof21aKXa1l9saTlXH8jIynaLljb4Z/zjQ8kSUxkaL9g/esICXE+OYNtIslcklMTp+gId9BUIbUlxMI6m1uJxuM8+vQxzgz3sHdXpQQrm9xyelhMl45lLZ1wvRp9JybQdY2mJaYzzyMdHmINSA/LJmE5Fn3xPlkHSCzLhbv3wm6wbIuHuk/w2p1SPGurc3kMrKyDy7V+U4Njo2kClR5cnpW9hir5IgRiM5Pbrk3ibPQsuyp3lboZYpMxDRMNmEglSt0UsQrLucybbh0rW/xyDMVITGQIVM5fbVyIjSAByyYxVflRiGK9Zud+ftl9CttZ34uZWB/HusbzqyMvwXStf8Bi2w6mexWXjQ3uYFHLGUsTm4YMCW0SLsNFxs7gMeTuRhTvuj0X8OOTL7C3tp7O6s059V0phVL5a15+Ubz8EMPUNclZ4HEUk4vlge04OM7kY07+4qvUrO8dB+VMHUfhOPntTB1XqWVPLzbdJr4Cs2GmLqFage+nWDmH3lMT7GoO0VBbwcRQcv6BtJmE3HTSIpuy0M+bvbMW1+ypImyOrUjHc/PbsoRkNL+qc3Q0RSZlLbiPpuXbOvtnPNX2qceK+beVtbFyjlTP3UIkYFlnE+kJItkIGhq1vlr8rsLVLBdT4apgIj0hdVZKJGtnebTv0ZKOwWtojIyO0xGcmR2mUGhoC16UZrdVQ6PF5eHEubMcebqfPa0rL/MOMNYXp6MxuPSOa0zT8hNkNe28f6NN/jf/vT551Zt6zNDzj+uGjq6BqWu4ND1fB0QHQ8/XAtG1/H6moaPrGpqh5V9HK26q7MRQkspFKtcu1+6m5f+M165G7FzjAwn+352P84b3HyKXsdF0jcr64t7beH9+SDJU48MXlBWTxcpIwLIOcnaO3ngvCkXIHaI91A7kS+YPJAfwGb6Cs3xSVorBxOC8k6PP9EmwUkJuw83lzZeXuhm8kD3OwY7lrRO0kAuAo8dHOLRndWXan3d0LtgnU1u3g2d/eg4rY3Pf3z4NwKHXri7YFWKlJGBZI/FsnJHUCJqmYeom7aH2eUFHvb+eeupJ5pKciZxBQyPgClDpqWQwOYjlWHhNLx3hjtK8CVGQw/pOF91IMqwvlisRyfDCQ30EKj0kJjI0dIZ49TtWHjAvx7n//jMAzDrf9HiZchQ4CmUrcJzJ/+bHj2pvvRBPW/FVd8XmIwHLGuiJ9eAxPMsONPwu/3TRt0QuwVBqiIZAAy5dqtWWK00KSYhtaKgrimMp3vrhS3j0O2c49NodRVW4nWLl8snA/3nPi2i6NrmWUP4m4NXv2Et1U2Dec7z7J0v1Tw7JYWj51zZ0NF1DKUX0B13Y42mQgGVbkIBllSKZCC7dteLFBgOuAAHX/D9WIRbiKMULXcfnba8OVtFYU7esY8hkM7Fc8fEMuq4RrvNx/a0XrOo4AKl4Dl/QhWFqGKbBmWdGGO6OzglY3B0hst1R4r/sA0vhv7SB4Fs7OXnyJLlcjt27d+P3+xn/95P5J6zDekmiPEnAskpj6TEpkS82zIWd+xbc/vTJ55cdsKwFYyLN+PGxuRtnT9OY+p7JTZMzbBYbjsrFczRcujnztAa7onj8M6fTrTIzJTGRwV/pXlGvykKuvHkXO/ZVAWBbDp/7wJF5aexH974N9oJHa8ZOpQlHXs3p/7iKiy++mIqKCu6//35e//rX48SzaC4d3xoumijKmwQsqxDNRgm6N36mhNh45Twk9NTJ5znQuntDX9Nb7aVqz9peKOYFQKs0VYNjI+oXefxm0TNnNoNM0lq/FY8nfy2nnhhi7+WN86ZjN7a+ie6znyfmPENd+GZ27twJQH9/P0899RQXaHW4O0Jo67TQoyg/ErCswnh6nLZgW6mbsW7sWAxrZARtqstVKfSKCsza1c0wEWsnmogR9ATweqQ+D4ByHMb6e9F1faaHx1EYpkm4fnP23pSSbuTzTdbl2JqGbmp0HR1lqDtKY2d+YnYweCFx3+X0VN2GdmwAXT1P1rL40Y9+hGmaJBIJbrzxRiJfP7Eu7RLlSwKWVWgMNDKQGNiSCxHmhobAtvF0zh3ussbHyfb04G5tLVHLto6JWJQTfafxuX35DZqi0ldJS0Pjso+Rc3J4Xd51amFh5TrRaKSnm9rW9pkge1I2lWRioJ/Kxq33t7qedFNfNGAZ7e2h+9mnqaiqYtfLX4lhLv+SoukaN3/kZXzrU09gumZ+Xz/IXUaaNq6OZviWfYiLzAy/d801ZDIZHMfB55v6e6F8P4hiXUhf2ip4DA9ZJ1vqZqwLJ57A1TT/5G5WVeUrf+ZyJWhV6azHkFDWytFau4MLO/flvzr2E0lFijpGJpfF6974gKUcB8jSiTiByqp5wQqA2+fHtheusCoKMwwNu8AK0Odeep7nj/yYzksuxeOv4Kf3fGHZx332p+d44gdd08m4FVUzn+Fup57frniBq11ebh3/Of/g/S9kuiLQm0bry5A+OU76+Dh2bGuee0Vh0sMiFrRYkp0eDGJHo5g1UjisGPFkggr/zGwIQ9fJWau7iLoNN9lshmwui+MobGXjdXkwjNIlfTqOw9jY2JzcEaUUgUBg5u64gKkS8CtWoilQW3XmlW7qOAUClr5jL7L3imt56eEUthXghQd/hid47WSSNTNLGgAjPbE5z/3Z1/Mz3V77m/kk8tnrE+03+rg7epjL9SG+27qfP0j+E8OfW/jz7Dssw9PbiQQsYkHKsnj67L1UeOcPT6ieXrTWHTBU3DEX6sFdeJs2rwT++dtmr8NyfOwlqkMH5vSCTO1baNvU8aZK2y/0OrO362swdfKJk89RF6rCmUwGVSj2tc5dgVvXDY6eeWnBHh0HxWh0jGsuunJ6W1VFmGM9p4il4ui6gaFrxFIJLtp1cNXtXQmlFIODgzQ0NMz7mU1MTOA4DoHAwtP4NV3DcRSGsbKrvzdQwXD3GXzB0LygJ5tOYRjrd7rbqsX4DFPDLjAkdOh113PgjnyRN69jQeNvoD95EkMDnwPvingxfQYu05iuueLyGZx9fnT6GI9+5wwA514ap+NQPvi4xvUcltfCCe3jnb1fYk/bO2n4yKX5dZP0ycWT9HwytV5gvSaxNUnAskpbdTVQd1sbgWefomPnPszq/GwQOx7HGhjA3HshRrB8Zkc5aOytf2Wpm7GkmmDlkmX1D7QtPtvn/BoshmHMO+ZzZ46trIFrYGJigrq6ugUDvMrKSkZGRgoGLLppYOccDGPlwWFtaztjvefQDR1NN0ApHMfBMA3JX1kB3Sjcw+ILzhRr+8B1BzDdBrajsGxFb9cEPB7DStkE6txoukllg59QjY9v/OXj089LRfPDOi8+1D8dsKBpqPgj7Ai2kiJD885fxVWx9WZgieJJwLJKFe4KotkoIffWqrSoud1obS1ohkG2uxsA3e/Hs3tjp8+KuUq5+OJshYZAHMdZdDhqsQDf8Og4GQu8Kz8tabpOTUvrZFvsfF/aBhQW26pDQov1sADsDPsID2V532t24fLM/N7vf/wcJx+PcdW7D3DR5XMDxT0vrycVz/ErH7yIz/3+kfzGWT8/0wwTiTzJyVOfmvy+fG6ORGlJwLJKtb5azkTObLmAZYoRDmOE12sdWLEeNuLiWSjuCIfDjI6OUrvA1Pd0Oo3LVbgLX/cY2Jm1W7NJ17dG8bZSWqyHZbaCgegy4+tENsGhew4B0OCt5NWN1/HRy/4Y0wzidkueisgr6tbj7rvv5vDhw4RCIUKhEFdccQXf//73ARgbG+ODH/wg+/btw+fz0dbWxoc+9CEikaVnPbz44ou8+c1vJhwOEwgEuOyyyzh79uzK3lEJlHNRse2gXHodtpUCUZFpmgQCAYaHh4lEIjiOQzqdZmRkhFQqRWVlZcFDGm4DO2OvU4PFSihHrVmV2/PNPmrGzs8WqvZW0xLexS9HuvD7OyVYEXMU1cPS0tLCJz/5Sfbs2YNSinvuuYe3vOUtPPXUUyil6Ovr46677uLgwYN0d3dz22230dfXxze/+c2Cxzx16hRXXXUVt956Kx//+McJhUI8//zzeL0bP1VzpbbSSr6b0XYKGMvmvS4ytOPz+fD5fORyOSYmJnC73Qv2uIwdO6+yrYJga8Vat1SsgrbKWifLvZmInXbgUrjrtXfxy75f8t3T3135i4otq6iA5U1vetOc7++44w7uvvtuHn74YW699Va+9a1vTT+2a9cu7rjjDt75zndiWRZmgYJCf/Inf8Ib3vAGPvWpT8157mbiN/1MpCeo9FaWuinbkvSwzDWRjPB81zEqfBW0N+woWTtcLhfV1YuX76/eJ+vAlDNPwIXjKDIpC49v/jl8VcOPs55sZxWa0iY3z58lKASsonCcbdt87WtfI5FIcMUVVyy4TyQSIRQKFQxWHMfhu9/9Lnv37uWGG26gvr6eV7ziFfz7v//7oq+dyWSIRqNzvkqpMdBIPBfnTOQMsWxs6SeINVU2vQ5L2aBz8CU7D9Fav4OJeHFF6IqxVZNMV2qLThaksiE/O6f32HhRz1vJ50OhUEqhazqOkl5rMV/RAcvRo0epqKjA4/Fw2223ce+993Lw4PyaDyMjI3ziE5/gve99b8FjDQ0NEY/H+eQnP8mNN97ID3/4Q26++Wbe9ra38cADDxR83p133kk4HJ7+ai2DMvEtwRY6w51k7Azd0W66Il2MpEZK3axtYpNcLdbgIr+cC+MTJ59lLDJBW33pelfE1lDbUkHT7jDPPdhbcJ+FP5ILf9h7XhrjzLMj8/ZovCAAWj5o0dG3bLkIsTpFByz79u3j6aef5pFHHuH9738/73rXu3jhhRfm7BONRnnjG9/IwYMH+djHPlbwWI6Tj6Lf8pa38OEPf5iLL76Y//7f/zu/8iu/wuc+97mCz7v99tuJRCLTXz09PcW+jXVT66ulPdROR7iDZC5Z6uaILWY5d67VgSo6mlqoCq7f7C65nmwPmqZR1xacLqE/7/GpsGOZn4f//KcXsbJOvuaKBjU7Apgeg1BjfvFORznTgYsQ5yt6WrPb7Wb3ZC2OSy+9lMcee4zPfvazfP7znwcgFotx4403EgwGuffeexedxlhbW4tpmvN6aA4cOMDPf/7zgs/zeDx4NsHqtKZukrWzuA13qZsiNkBX/zmSmZkgNV+WfI1PvJo+pzCcrmnTlXP1yWhmI7rTXR6T7pNTSbPFvMeZLM5sLMPA8Zmqp1NB0FoON61JYKVA06G5cmZZAU2b217TtXWnUPtDbpLRhQOWJTn5Kc9nn3uG0XNnsbIpXv6Gy7jo2nyv+K//6SsA6I33wrdmelhkSEgsZNV1WBzHIZPJf5ij0Sg33HADHo+H++67b8mZPm63m8suu4xjx+ZW5jx+/Djt7e2rbVrJNQWaODZ+jJ3hnRK0bFJKKazBQVQmA7qOq7kZrUBhtHg6wYWd+9a1PQfby6Nw34627VWbp3s0QWXN9qy2Ggh7yCQssmkL93lF/QrGlrMeePTb3yRYU8vOl13Oo9/5fwyc/AWwc87u+lRnv5KkW1FYUQHL7bffzk033URbWxuxWIyvfvWrHDlyhPvvv59oNMr1119PMpnkK1/5ypxk2Lq6uunql/v37+fOO+/k5ptvBuCjH/0o73jHO3jNa17DNddcww9+8AP+4z/+gyNHjqztOy0BTdPYX72f05HT7AzvXPoJoqzkhoZw4nFczc3oXi/KtsmdOweahrutrdTNWzEZzhHFqKjK92anYtl5AcuUxT5SsZFhLn/Lr6FpGt7QxfQc/QLwTgCcjI01miKXyvdMOspB13Rydo7nR59HqfyCnkopHOXM/BsHn+njcO3h1S2WKTaVogKWoaEhbrnlFvr7+wmHwxw+fJj777+f6667jiNHjvDII48ATA8ZTTlz5gwdHR0AHDt2bE4xuZtvvpnPfe5z3HnnnXzoQx9i3759fOtb3+Kqq65a5VsrH4a2dbuLtyo7GgXHwbNzJtDUDAN3eztOIkGutxfXDklqFVvfVEBQTDHb2TP3Drz6Gn74+b8jUFlFfPglDl7zvunHxr72EukXxxg1x2EPZE5PEKgPEMvF+PXv/PqSbfv2W78tN4PbSFEByxe/+MWCj1199dXLyuxeaJ93v/vdvPvd7y6mKUKsK3tsDPdkkH0+PRAgNzCwsQ1aQ3JDKooye2n08x+afmzhc78Cduw7QPPe/VjZDKeeeYJA5czaQipj49lTSd3VO+Dn4ORs/sve/8LFdRcDoGv6/C90TkVO8cGffJCMtcLcGrEpyVpCG2DT1AkRMwwDlcuhFUoa19Z/Qb31IkNCohi5dH65BNO98p5iTdNweSZzGmefDnWNzIkJnEvzOX5K5ScrHKg5sOjxYjmpd7Udbd6z7iYxkhqRCribkKulhczJkyjLmvdY5tQpXA31JWiVEBsvEcmABv5Q4RmfK1VxRTMAub4EAEqWORGLkB6WdZTMJYln43SEO0rdFFEkTdPw7N9P7uzZ/G3f5DxW5Shcra3o7vmzvqQnTWxFyWgWX9CNbsy/v52apF4wv6XAA5H7u8icmqD6N/M9KWbYA8Mz+zupFKNf/L/Y0Qi6x0P1u9+NWVUFQM7J0R3tXvX7EpuPBCzrJJ6NM5IakWBlUsZx6E3npk9w1S6DStfafPzWawqkpmm4t8D0eiFWIzGRIRAusizDIrG7k7aJPZSvnBv/ef6/2TP5GaVTf8kjn/88lW99K+6ODqzRUfr+2x/S9n/zOZT3PH8Pn33yswD4Xdtzqvl2JQHLOsjZOYaSQ+yslOx1gJ50FkcpOn3u6RkHw9kcxxJp9vo9Mi1xO4kNQjae/3ewCdxywSl3iUgWf6hAoc7Jv139+f8HQ0+A4QLTg1Z9a8HjpZ+fWbJkKmDJvDgOe5npqpl3DzKzIZHLDx99+63fpj0kNxTbiQQs6+Bs7Cy7KjfXitPrZTRr4dU16s4bQqlzu6g0Tc6ksuz0r65qsQzFFM9xHHR9A1PYkmMQH4JQMwQb8tsi52CiG+r2y9SlMpaOZ6cXQVyIhzT64DPwps/kNzz3b7Q/+Q8c44YF+z6NsAfDa9D4R5cx8o9HyZyeKXMx1Vta+7735oeEYlF0j4fmv/7r6X00NJoDzTKdeRuSgGUd6Jt4Bslai1o2nQUCEpeuYcmUlQ1nGDqWrXBv1MfUcSAxDPX7524Pt+QfGzkOdetbIXgxE8ks48lcwbBX08B2tu/nNJu2cRWYIaQBOdxoqTEYPQW+Kuh9gtEd1y56TM3Q0HRteuho6qZjKodF9/up++AHCj5fKuFuTxKwrAOf6aMv3kdzRXOpm1Jybl0jYdkEzMInPLGxXKaOZTu4N2r9m4luqCmwpICul3ye9UQyR2dtoKRtKGfBGi/R0XTBxx10cq/7S4zj34B0FC76dWIDNcBLSx98smdt6jzgLGOWkK7pErBsU9IVsA4aA42E3CFOT5wudVNKbofXzblMjphlz9luOYrn4ynafbLG0kYzdA3L2sATvq8KkqOFH9elEnQ5q2zwMzG4xMrz3iq44vfhmtuh8dCSxzz/06epxavpztlX05ZVpFRsPdLDsk4q3BWMp8exHRtjm5+Q9wW89GeyDGet6VlCGnAw4F2ThNtyudvaLGkYhqFh2RtY78JXCSMnwPSCNzT3sfHufEAjypY/5CYVyy74WMFhtALbnYxNtiuK1hI4b8fJgGUZf8sasjjidiUByzrKOtltH6xMafJIT0q5cJk69kYGLAC1eyDSm0+8nYrslIKK+vlBjCgr2ZSF21fgUqEtvvDh+T0hKmuDoVHx2hYAfBfWgq2wQwHIsKwuFm2pFxVblgQs62QgMUCdv67UzRAbaLP0UhuGvrFDQlPCsljkZpSM5AvHLWRmmaHlf57cbUEqLs+vJ1TxiiYqXtFELBuDf13mcbTy6VUVG0sCljVmORZnY2cJuoKE3HLnuJ1slnF1t6kTT+ZK3YyysVmG8krBcRTdz4+y62VruBTFAj/wqVlCRxIP0f9MBFQ+AVcphaPyvYGOclAovvzCl/GZvrVrj9g0JGBZIwOJAdJWGkMz6Ax1SjG07WiT/MoNU8O2N0dwJUqr99g4yUiWvZc3rN1BFwjsvaaXXZlWnjKO8syxF9DR0TQNTdNm/o2Gruk0+Bu4tOHStWuP2DQkYFmFwcQgKSsFQEOggcZAY4lbtF1tkkihTLh0fWOTbsvcJukYK4njjw4QrvPR0FGot3gqH2nhzctl6ib/u+9PCV3TSnAyv0WI80nAUqSpNYIUigZ/Aw2Bld15xLIxxtJjGJrBjood0iOzKnLFKYZhbvAsIbEp2bbDqaeGufjaVvpPRvjxl17gbR+9lIqqmUKQWoH8V61QILMYrdgniO1GApYiZOwMw6lhOsOdqzqOUorBxCC7q3aTzCUZTA5K74zYMKahb+vKreeTe4WFRQZT5NI2O/ZV8eJDfcTG0kSGknMClilL9VLFxtJ8/Y5HyVgKvcAPfHJBdCEKksJxReiL9606WAHojffSGmoFIGtnyzaBTC2j6mQ5kJNccQxDx5GARSwhncjXXgmEPeSy+XNBLmsv9pSCEhMZMgmLgzUedrdVrFkbxfYiPSxFMLTV1VTJOTm6I92EPCE8Rv4uJZaNlW0Jf22TxLNyh1w8CVdm6JqG7SgMXT5Is1m5fJBiuHQiw/lKt84yk7Wnhrifua+LYz86hzUZ6LSEXHg9UptKrIwELBsgkokwlh5DQ2NX5a45+SoOTtkWl5PT99Ylv9sZblMnZ5fv32GpjPUlAEgncoz0xIH5U/cL3SzoHp0HvDnefbiJoNdE0zQ8AZOKF0YKv+BUGWwhCpCApQg5Z/m1K2zHpjvWjY5OyBNak6GkjSbnjuJspp6ezdTW9WbqGlnbwbtRi0FuEkPdMQCee7B3eptaYJRYLfBZ0jWNR70Wf/MrHeyonBnyHnhxkTWlJGIRS9gcff5lZDnFwcbT45yJnKEz1ElHuINqb/Wqjlcqm+eatnlaWi7K+GO34VymTs7aHPlaG+nAq/LVaM88PUzrwfw5bF4Py0r+9gp99iReEUuQgKUIbaE2jo8fx3KseY8ppeiJ9tAV6cJRDrurdi9rqrLf5c+XpRarUB5nOQkCNie3oZOTQnrzBML5PLtULEfHoVqg8A3Wsm+8plY+XexxIQqQIaEiuHQXe6v2cjZ2dt6dhaMcWoItmHpxP9J6fz3d0W6C7uBaNnWbkR6WYsmQ0AyXkc9hEXNVNvinpxp3HKrhZ19feEioKEoV/vDJZ1IsQQKWImmaRnuofW2PWaZ/qXKzs3VJb9AMQ9ekLs0CdF3j1//0FeSy9vRqzaZrbqf8is5cBfv1pRCLWJwELCWmlFpwiEmUlxfPnMRm8RoU4YAsdim2lurmAADRkfwSJG7/3EuGpsFJl82vfP4X2IDtKHK2QyqX/1uZmikef7SfiX87mf/moPSwiJWRgKXEuqJd00Xkyo/c7Uyxsbmwc1+pm7FmdKk5IoqQSeZvqjy+uZeMdxxo4ps9SQ7trsXnc+EyNAxdw2Xo1ATcNIa8+R2nTiUa6C6dbG8c9465BeQ0IDeSIvXiaH5/pUBNdrpM9bxMbgPw7KrECLrX5f2K8iQBSwlFMhGC7iAu3VXqphRQXhe1qcS+qSqtKn9Ww7JtMrkc589fmNln8jyn1Kzv1eS5b+Z/s4+rpvdROCjG0wmGx1PTJ0vlTJ5MmWrTTBvV5As6atbrTr5eS3MQl1n6XPd02iaeyM79mc36ZsEkSjX/n1PvDZh+v8x5bGabw+S+s7epWT+zSY6j5v7emMmdmPodKpV/LoBynOnrm8bsNqhFUyZm81SU699g6SSTXRw9+nscOvQPZFNhANzeuZeMK1uqGE+5+a/X7p1O0l2IpyM0PQso+uOzxH7ex46PXTFnH73CRerpYVJPDy+rfRWvaaHyDZuvXIRYOQlYSsBRDr2xXhSKtlBbqZtT0HDSj+p9cWbD1Il/rTpeipktoM3+pza9QdMAp5ZnB7vnHXLqQjU1W1LT8s/V0KaXrof8kPr0jC5NY/b/9MnYor6mgWzayu+nTbZgshkaoBvazBopOtOPa1p+u67nX/vE6XEO7q1Z9o9ovdTX+Bgay3fzF/o9LDTLTTvvm/zPVGfWj2/65zz7e8jX5tA0ffLxqZ+RNn3Q6Z/Z9O9n8iF99u9pgX302ceafOXp9iwv6J4YSi5rv+3k1OnPEE8c4/SZv0Ub+yiaBv5QgR6NJc4JroYAzX92Bcp2iD/UR/znvfP2qX//RTgpC2Z9Jpj8Hc98aPL/Hfr7p2YiVrFtSMCyQZysjR3Nr80xnh4jl03iMl04FQ66Xvo77oUozcOeHftL3YwtpVxyCtuaZVaaKCwae46hoe9hGBUMDv4HNepD6IY+vzT/5LfaMoYY9cnhJN3vWrDbq394kKeeegpd1/F6vbz61a/GNOUSJWaU55VyC1COwhpLkxtJkRtJ4aQsXLU+XLU+6lt2sHPnPhqadnDu7BnOdp3i5PEXSt1kIbYtmeY9V0/Pl/D7O+js/CC67mH3y3ZgWw5HHzg3Z78VFb6cO2437dFHH+Wmm27ipptuYteuXXz3u98tfAyFXL22IQlf15g1kUHlbDRdw6j0ohmFz4Rul4e2jl3EohHiiegGtlKIwqLDQ9h2PslSOYpwfT2GKTke20k0+hzV1VdhW3Fcrioqqjz4gq55Q2xquodlbV43lUrh9/sZHx+nrq6u8I7LTU4SW4oELGvIGk2hB1zolYWTzxYyPDTAzt1bZwaK2JxymTQTgwNUNTZjuvO5CkopIoMDGG4XweraErdQbBwbXfeQy03gMsMopUgnLLyB8y4ZUwFLMcGDBsp2iPwon3fmaQvi3VfNtddey8MPP0w2m2XHjh1cccUVBQ8h8cr2JAHLGlKOQvcW/yNNphLr0BohihMdHqaurWPONk3TqGxsYnygD9vKSU/LNqHrPmw7hWVFMV2VZNM2ylF4AnN//1NDQsUED562EK7mChKPDuDEsmQmA5ZgMMi11167vINIxLItScCyhnSfiZOyppPLlmN4eICWlrWtnLtWyrUCr1gfull4teLKhibGes9R01KuNYPEWjIMH46dyvewuKrIJPIr1XvnBSz5/xbTw+JuDdLwgUsAGPvGcazhFczQWmpNIrElSdrSGjIq3NiTf9jLFYtFqKwq/TTXhag1m78sNgPHLlzJNzo8RLi+YQNbI0rJ5aoikxmcDFjCpAsGLFMRywpfaKUrNE/VKRDbigQsa0x36eSGk6hlLFff39dDQ33zBrRKiKVVVNUw0tONcuZ+dmOjI2i6Np3XIra+cOgSItGnyWVHcbmqpgMWz3k5LDNDQisPHlZ0W1Qu9QHEhpIhoTVmhD3oSmGPptE8RsHS0Y7jkEolaWqWLnZRHjx+P25vKxNDAzMblcIfrsLj95euYWLDVQT3Y9sJbDuBywyTGcvPGju/h2Um6XZlr6NNLQddJElh2Z4kYFkHmqZh1vqwRlJQoD5X1+kThCurmJgYmy7zDgrlTJVxd6bLmDtOPuFtugz8dIn5yTLlk4/NVGud/L/papGz/junDOj8x9XUUzWNWGJ8TX4eYsbwSJIXZp1o1SIn/NmPzT6nB/wuOlrXZ6FFTdepapRev+1OYyafqav78zjWF9jzljQPPuhBOSbKcWHl3Aw8826giW+f+jaOYeM4Dg4OjlrkC4dXNr2SS+ovWflQkkQs25IELOtpkb+nhqZmMpk0jnLygcZk3XFdN9D0mfLxum7kAwhdQ9d0dE3PJ8NOlief+l7TtOkAB5gOgCbjGqbWVVnwMTWzbsvsfzi23FWvtatftfoeteePja5BS4RYTP4c0Nr6bnTNRTpu0PNCDEe30E2LDCP4q39Kf/gIL5kVPP3oj9HQMDQDTZv/X13T0cmfv2LZGE8OPskXrv/C6nJYJKFh25GAZZ1YIyn0RVYSDQSCBAJrWx599rosk1so/N0yeAvPGhFCbF2Z7BCgsbPzDzDNAAAXXjzz+ANd38E6/VPedvPr2N3yaxiagb7M6nF//OAfM5IamdmwknwU6WHZliRgWWNOxsKeyGBUetA9csEXa0/O02K9xWMv4vO1Twcrhbh0V9GrzWuahjO1/LamraiHReKV7UkCljXkpC3seA5Xw+J/5EKshkyQEOstFn+RYMWBZexZfNSgo88KWFjhkJAUYtmOZBRwjThpCzuWxVXrK3VThBBixZRyiMWeJxi8YJF9Vh41a5o2PYFgOveuWBKvbEvSw7IGrEgGbIWrTpJUxfqTrnCxnlKpbmw7TjB0aJG9pgKO4u95dW1WD4u+smnNUjhue5KAZRWcrI09nsYIutHDssaK2BgyJCTWUzR6FIDQYj0sq6iCrWv6ZOkGhaM5KHulSSwrboLYpGRIaIWs8TROIp+vovslWBFCbA2x2HN4vS24XFXrcnxDMzg6cpRL/vkSvvTCPzGWHCv6GJJ0uz1JD0uRlOVgjaXRfWbBKrZCrCc5UYv1FI09RzB44aL7rKYk/28f/G3aQ+14DS/pn/Sj4ksvY7JAA/LDSWJbkYClSLnBJK7mwKrWzhBiNWRISKyXqYTbjvbbltpzxa/RGe6kM9wJwL0PfQldreBcKkm325IELEXSPAb2aBpzi84G6jk9jm3n73iSsWyJWyOE2EiWFcG24/h8bcvaX1tlVoHSFS7bJHl0mFxvfMFg/Py4xMna4Ch0j1y+thv5jRfJVetDWQ7Z3viW7GmxbIfOPTWlboZYxBb7yIkyYpqV6LqPdKZ/0f1U9Odr8nopTw6f7WHsX16aaUONd+Z15r1wfpmSiiub8R2uXZM2iM1DApYV0EwdV4MfeyyNWbM+PS3xeJxMJgOArutUVlaue3A0MZ7C45OPRLmTISGxXjRNoyKwh0T8+OI7ZroAqAguNvV5aSfa+znie5TP3/h5NFMHU9tyN4Fi7cgsoZXStHW5cFiWxeDgIIZhUFNTQ01NDaFQiJGRERKJxNq/4CxjgwmaW8Lr+hpCiPJmmiFsJ7X4Tk6aJ5MGpqtyVa+l6RoxVwLdZ6K5dAlWxKIkYFkhlbHAUSh7BRnuixgbG6OhoQGfb6bnxjAM6urqSCaTOM7avt5suiEni81AzuliPWm6C8dZPH8tnhkl4+RXjF8NHX1VNV3E9iL9/yuk+11oPhN7PAPO5B/c5N+uchRmpQfNVfzih4ZR+Dk1NTWMjY1RW7s+Y7eOIyeOzUCGhMR60nUXjp2e/v5EIs1AJsdFIT8hM39+imXGyCkNn2t1Q+IrLs0vtiXpYVkFTdMwq72Ytb78V03+y1XnJzecWtEf4mLPicVihEKh1TS5oEQ8QyZtrcuxhRCbh8/bSjT2HMnkGT7TNcBPxqKkHIePnezlhXh+qMilKS5puAyP4VnVa80p0y/EEiRgWSdGyI1KFR8A+P1+RkdH521PJBLYto3bvfbF6pRS9HZHOHBR45ofW6w9GRIS66m19b9imiGePfp+hrMW72ut5/raMLfvbOIPXjpLMpdER+F1BVf9WhoaDhKwiOWRIaF14iQtzLriu0v9fj8ul4uRkZE5CWher5fq6uq1bOK0kbEUdXWBdTm2WHvSgy7Wk9fbTHv7e3nppT+hrdHg02f6afN6+PlEjLsPtqOwMTWFpq1+SZKpdYWEWA4JWNaBncihuY0VZ7y7XK51y1NZSGIsRdvO9Vk3RAix+bhd1YDi1iYXYyrMUDbHWxsq8eg6J8dP4tfB76lb9etomiZDQmLZJGBZI07Wxo5M1k3xmpiVqxvb3Wi6IaODm4UMCYn15jhTNaC8NJouGj0zvSnDibOYGtRUtK/6dXQkh0UsnwQsy5AbSoKen8B3/hIWU52ZmqnjqvNvfOPWQFWdn76eCM2tUoNlM1Aqn3ckNSvEekmlzmEYfgxj/jktmYsB4FngsWLpmkxrFssnAcsSlOWgcg7uHRWlbsq6CVf6GB9OlroZYpmamiro6Y3R1rI+M8bE9qaUYnziYSrDL0fT5ve82o7CALQ1CDRkSEgUQ8YBlqCZOmadj2x/ArVF65SceGGYhubVZ/yLjVEd8hCJy8KUYu2NjT3ET366m7Gxn1FT+7oF93Gb+QT9rL36mxxJuhXFKCpgufvuuzl8+DChUIhQKMQVV1zB97//fSBfofWDH/wg+/btw+fz0dbWxoc+9CEikciix/yv//W/omnanK8bb7xx5e9oHehuA1eDn9zg1uyFMF06vsDaT5cW60eGg8R6eOrpWwAIhS6iqfHmBfcZSA4BUGGufh01DU2GhMSyFTUk1NLSwic/+Un27NmDUop77rmHt7zlLTz11FMopejr6+Ouu+7i4MGDdHd3c9ttt9HX18c3v/nNRY9744038qUvfWn6e4+n/BJWNV1D9xkoy8kv0rVFpBJZXF4ZGdxsJFwR68HtriWbHeGyl/9bwX2imVHqANPwFtxnuWRISBSjqCvVm970pjnf33HHHdx99908/PDD3HrrrXzrW9+afmzXrl3ccccdvPOd78SyLEyz8Et5PB4aG8u/aJkR9GCNp3HVrs8KzaXQc3qcvYcaSt0MIUQZaG15F91nv7joPoPRl9gFeLxNq349qXQrirHirgLbtvna175GIpHgiiuuWHCfSCRCKBRaNFgBOHLkCPX19ezbt4/3v//9C1Z6nS2TyRCNRud8bQTN0GbWDdrkMmmLl54ZpGXn+hSjE0JsPh5PI5Y1gW3PX635e6e/x6F7DvHL3iMAmMbqJyLorDyHpSfaQzK3NYfpxcKKHgs4evQoV1xxBel0moqKCu69914OHjw4b7+RkRE+8YlP8N73vnfR491444287W1vo7Ozk1OnTvE//sf/4KabbuKXv/xlwYUA77zzTj7+8Y8X2/RVU5azqQIW5SjQZvIdrJxNf28MO2eTzdh07KvG6119tUohxNbg8eR7ujOZAfz+zjmPDaeGAegItgCn0PXVD91r2kxpfqUUI6mRBfezlY2tbCzHwnIsBhODvO/H7+OdB97JH1/+x6tuh9gcig5Y9u3bx9NPP00kEuGb3/wm73rXu3jggQfmBC3RaJQ3vvGNHDx4kI997GOLHu/Xf/3Xp/996NAhDh8+zK5duzhy5AjXXnvtgs+5/fbb+chHPjLn9VpbW4t9K0WzRlO4GjdHCfuXnhvC4zVRtoOm5wMWw9BpbA7ilpwVIaZ190bRbIVUIQLv5DBPJjM4L2A5XHcYgN2hHWCdwliDHBZd0xlJjfD2/3g7L469WPTzV7v4othcir5yud1udu/eDcCll17KY489xmc/+1k+//nPA/kVhW+88UaCwSD33nsvLldxd/A7d+6ktraWkydPFgxYPB5PaRJzN1E12IamfHdtVc3mLGYnxHqxLIfTZyfIZhVKOTQ1VhC3JY8C8km3AJns8LzHWoOtHKg+wKnhX3JhlY7HU3zum3Icko8/jj0+gf/yy3hd6+s4Gz2LqZtc23YtAVeAtlDbvOdpaJi6OfOlmdz6w1sJuDbHDaRYG6u+1XYch0wmX8Y5Go1yww034PF4uO+++/B6i4/Az507x+joKE1Nq0/o2s40NGxHTsJbVSSa4flj+VyvhWY4T6UFaNrC/4Z8HpOnyN62zTybevbPobM9hG9Wufn4SKJErSovlp3/OczOT5nKMan11fLVm77Et468ijSgaQsP2S9m8C/uwHfpyzDr6hj667+m9d238hdX/cWK2uoxPDK9f5sp6mx1++23c9NNN9HW1kYsFuOrX/0qR44c4f777ycajXL99deTTCb5yle+MicZtq6ubjofZf/+/dx5553cfPPNxONxPv7xj/Orv/qrNDY2curUKf7oj/6I3bt3c8MNN6z9u10l3a1jjUwmoxkaRtgzPdyyngZf+CH+ylnrdihQLFGa3YZRHMb7Fq5zoKGRzMQ53PnydWixmCcTg9jgzBVfKQg2gGdlBfuuvKx5DRsnRF4uNwGAyzWzGGrn7d8DYF9DkNHoGXT+gLe97Ie8uchjq1wOTIPwG98IgGaaDP/d39Hyt3+zorZK/Zbtp6iAZWhoiFtuuYX+/n7C4TCHDx/m/vvv57rrruPIkSM88sgjANNDRlPOnDlDR0cHAMeOHZsuJmcYBs8++yz33HMPExMTNDc3c/311/OJT3yiLGuxGKGZNinLwR5PM/tvRvOZGIG1T2KtqN6LK1SF27/0KPto1iJi2ega7PS4MRcJqE70FT9mLFYg2g9ODmrn/l0wcRYycQhJb6IoD9Z0wDL/XHPFrhq6zn6fI+eu4mjyZUUfW3O5cNXXM/TXn8Goribz0ks0/vmfrbitSik0qUi0rRQVsHzxi4Xn51999dXLmp42ex+fz8f9999fTBPKhmbqmDVz67E4ydx0D8zUuzTDHjTX6nJfAo0dRM69sGjAMpGzGMpa1LlNdvo9OEpxNp0l5yj2BFafHCdWIRuH2j3zt1e2wciJjW+PEEuaOWcdbglzsCnEx958AT/4z59y5NxVrLR0Yc173oMdieDE45j/9V2rGtJZspdZbDkyXWQN6X4Xun+mh0UphR3J5qvjTm1c4VDSYuPFlqMYylrsnRWY6JpGh89D1nE4mUyz2y9BS8kssIDcNOXkh4fkxFtS8uPP07T8JUEpa3pbyKvTN/I8L770dWwnverXMMJhjPAazMlSSA/LNiMByzrSNA2zcu7Q1kqHkhb7s+xJZ9ntX3gIza3r2AU7vmQMeEM41uKPy9VSlAmvtxlNMxkY+Hd27fpvACg7wTVjX6fyFy9yiebj142f0EPpc6gUMiS03WyeebpbxNRQklk786VpYI2kyM36Urm5M3wUhWf8VJg6Mcsu+LhZ6G9ayR/7hgg2wtCLkJtVPTSXym8Llv+SFGL78Hqb6ez8EF3dn2Mi8gQAO63TZDQ33rd9hdBvPs7l+ku0x0+XuKUyJLQdScBSBnS/C7PWh2vyy6zxYieycwIYKxcrmCNU53bRn82RWqCWRE86S7jQ0gjyt74xvGGoPwDJURg9lf9KjOS3eaVcmSgv7W3vIxy+mBee/0MsK05XupIWaxQrMYBKjePC4oH4AP/w9D+suKz+Wijla4vSkCGhMpQfSpqbc1JZfRGpVDezh3Hc7lpMMz8tdn/Ax9lUBkvNxCEOijq3i5BZfL0EsQ7CLaVugViA7Sh0uVOfpusmBw/cxaOPvYkTJ+7ACb2Z/8heztWP/ysu31PcZb2dy3d6uPuZv2IkNcKfvOJPMPSNP8fIkND2IwHLJqHrHvz+jjnbMtkRsskR8iGKok5z4fXtWHY3qeMUHkYSYruwHAdjA+opbSZ+fzt79/xPXnzpdlpDV/JvZy7n6O4rOLT7DXT/5H7+sO0SXrM/wMd/+XHG0+N88jWfLEmZfBkS2l5kSGgT87hr8fs78fs78Ps78XjqSaW6SSbPTH51YVmFK3hKh6oQ+R6WxeoVbVdNTf+F2trXc3Xtn1HrG+V/fl8nZ+WHnceTWW7eczOfveaz/Lz357zvR+8jmo1uaPukDsv2IwHLFqLr7ungZSqQse0EyWTXdBCTzgxMj/0amnSwCWE5SnpYFqBpGgf234HHyPK+w/9Ezzjc+f18sUlTz186Xtv6Wr5w/Rc4MX6C3/nB7zCUHNqw9knS7fYjAcsW5/HUzwliXGZ4shemC0dZdI0kGIquvraCEJuVbavpC7CYy+2u5eDBT9MS7OdDr4H/9/i5/HZz5ud1cf3FfPmmLxPJRPjt7/02XZGuDWmbJN1uP/JXus0Yhm8ygOlgX8uFdNQGCHhMukYS01/dowlysnqt2CYsR2EYcqdeSEP9G7jssm/z/tdfx+Wd1cD8YGFX5S6+8oav4DW93PL9W3hu5Ll1b5ck3W4/ErAIAh6TjtrA9FdrlZ+BSHpOEDORzJa6mUKsC8lhWVooeCGG4eF//eYlXNAc4oLm+dPxGwONfPmmL9MWauPd97+bh3ofWtc2yZDQ9iMBi5hH1zVaq/1zghilmBPA9IwlcRzpkhWbn+U4ErAsU33Qy3c/9GoONocWfDzsCfOF67/AZY2X8YH//ADfOf2d9WuMlObfdiTrUixLVcBNVcA9/X3WcugZTzLVM6yAhpAHv1s+UmJzsSXpdk35TB9/e83f8vFffJzbf3Y7Y6kxbrngljV/HRkS2n7k6iJWxG3qtNcE5mwbjKYZjGamv/e7DeqDHum2FWVPPqNry6W7+MSrPkGtr5ZPP/5pRtIjfPhlH17Tn7MMCW0/ErCINdMQmludN5m16B5NztnWXOmbM8NACLE1aZrGH1z6B9T4avjUY59iNDXKx678GC598YVel0tmCW0/ErCIdeN3m3TUznzEHEfRF0lhzVo+OuxzzRlqEmKjyXVvff32wd+mxlvDnzz0J4ynx7nrtXfhd/lXfVzpYdl+JGARG0bXNVqq5p6oJpJZzowkpkeiDV1jR6UPXXIKhNgy3rDzDVR6K/mDn/4Bv/vD3+V/X/u/qfRWrvq4uswb2Vbkty1KqtLvpnPWbKTGsJee8eScGUnxjFXqZgohVunK5iv50g1f4lz8HLf84Bb64/0rPtbUcJD0sGwvErCIsuIy8sm8s6dUJ7P5irxnJr/6IykZvxZiE7qg9gK+fNOXydpZ3vn9d3Ji/MSKjqMmV0KTWULbiwQsouzVB7101AbonPyq9LnpHp3bC5OxZOVpITaD9lA7/3zTP1PlqeJdP3gXTw4+WfQx5IZle5KARWw6PrcxpwemvcbPSDw7J4AZjWeWPpAQoiTq/HV86cYvsa9qH+/90Xv56dmfFvX86R4WGRLaViRgEZuepuUTdWcHMaauTw8hnZmszGtLZV6xALnmlUbQHeRz132O17S8hj848gf824l/W/ZzZUhoe5JZQmJLCvtdhP0z9R4s26F3PIUzqyu5psJN0Ls2NSGEEMXzGB4+/ZpPc+ejd/Lnv/hzRlOjvOfQe5buOZn8M5Yelu1FAhaxLZiGTlvN3CnVw7EMo/EEkD//eUydprBXToJCbCBDN/iTV/wJNb4a/u6pv2MkNcIfX/7H6FrhAQDpYdmeJGAR21Zd0ENd0DP9fTpn0zWrMq8GNIa9eF1GCVonxPahaRrvv+j91HhruOOROxhNj/KXV/0lbmPhopJTAYvYXiRgEWKS12XQWTuzPpJSiv5ImqzlTG+r8JrUVngWeroQYpXevu/tVHur+eMH/5jf+/Hv8bfX/C0V7op5+0kdlu1Jkm6FKEDTNJrPS+Z1m3OTebtHE5LMu8nJDNny8vr21/O56z7H86PP8+77381IamTePjIktD1JwCJEEUJe13Q9mM7aAC1VfnrHU5yZNaU6ms6VuplCbGqXNV72/2/v3mOaOvs4gH9PaekFSrk6hYK+0w1hBll8M2W8G7vIOsMfzuziJOAFskXHKzPbdBJj3DQTDca4uasZw/EatgkL0S1BhgPe7IKELWMz0xmXyeBFNwYI5X5pz/sHa6XcbLHQ0/b7SfpHT885fZ78csqP5/zO8+D4o8fxV99fWF+2Hk3GJpvPrSMsTFi8ChMWolvgIxMQFaKxWV5gcNhsMydMcwdn5iVyVHRwNP6z6j+QCTKklaXhYtvFcfvwlpB3YcJC5GSh/kqb20ghfr5oaOu1uZXUP8SZeYluRq/Vo3BVIcL9wrGpfBNqr9UC4C0hb8WEhWiGWYp5La8FIRpc77WdmfevLs7MSzSRYFUw8g35WBq2FFvObsGZhjM3RiyZr3gVPiVENMsEQcA8ndpmW1f/EBpae6wPa8oEICJQDbkP/6eYabyrIH0ahQZvPvQmdn+7Gzv+uwP/vvvfADjC4m2YsBBJgFalsJl112QW0Xy9D6ZRtS/BGl+b2XuJvInCR4H9/9qPYFUwjv5wFAATFm/DhIVIgizFvKO1dQ+gobXH+l7uM7KGEgsPyVvIBBm2/3M7wtRhOPz9Yajl6psfRB6DCQuRmwjxVyJk1KR1A8Mm/N7Wa72NJIoi5unUUPtyZl7yXIIgYNOSTVg5fyUi/CNc3RyaRUxYiNyUUu6DBWNm5v3TOIA/jP3WbX6+PpgToHJF84hmVKQ20tVNoFnGhIXIQwiCgLk62+SkZ2DY5jaSIADhgWooWMxrxSlyiNwDExYiD+anlMNPeeMyN5tFNHf0YXjUcgJBGgUCNRMvMkdEJBVMWIi8iEwmIDLYtpj3es8grvw9CiNgpOA3IlANmYzFvEQkHUxYiLxckJ8vgvxujLAMDpvRdL0XlkEYURRxW4DKZqSGiGi28ReIiGz4ymWYH2JbzNvSNYCWUbPxqhU+uC1AyUeqiWjWMGEhoikJgoDbxjxp1Ds4jIa23pHP/94WHqiGr5zFvEQ0M5iwEJHDNL5y/CN06mJenVqBYD/pF/NykIjIPTBhIaJbNlExb0fvjWJeAPARBOiDWMxLRNPDhIWIZkSgxtfmcekh00gxr2XeExFAmFYJfxbzEpEd+EtBRLNC4WNbzAsALcZ+/DWqmFelkGFugIrFvEQ0DhMWInKZscsG9A2arMW8FuGBKijlXB+JyNsxYSEiyVD7+uAfY9ZHau7ow5DpRjFvgEpuswjkreLU/ETugQkLEUmWIAjQB9kW83b2Do0r5o0IUsOHxbxEHo0JCxG5FZ1GAZ1GYX0/bDLjf6Nm5gWAUH9faFWKCY4mInfFhIWI3Jp8omLern60dg9a3yvlMszTsZiXyJ0xYSEijzNHqwK0N973D40v5p2nU0GlYDEvkbtgwkJEHk+lGF/Me7WzH4PDZqiZtBC5BSYsROR1BEFARKDa1c0gIgdwpTIiIiKSPCYsREREJHlMWIiIiEjymLAQERGR5DFhISIiIsljwkJERESSx4SFiIiIJI8JCxEREUmeQwnLO++8g7i4OAQEBCAgIAAJCQkoKysDALS3t2Pr1q2Ijo6GWq1GVFQUsrOz0dnZaff5N2/eDEEQcOTIEYc6QURERJ7NoZlu9Xo9Dhw4gDvuuAOiKOLDDz/E6tWr8cMPP4xMdX31Kg4dOoTY2Fj8/vvv2Lx5M65evYqSkpKbnru0tBTnzp1DeHj4tDtDREREnkkQRVG8+W6TCw4ORl5eHjIzM8d9VlxcjLS0NPT09EAunzw3am5uxvLly1FeXo6UlBRs27YN27Zts7sNRqMROp0OnZ2dCAgImE43iIiIaJY58vd72msJmUwmFBcXo6enBwkJCRPuY2nAVMmK2WxGeno6tm/fjrvuumu6zSEiIiIP5nDCcv78eSQkJKC/vx/+/v4oLS1FbGzsuP1aW1uxb98+PPvss1Oe7+DBg5DL5cjOzra7DQMDAxgYGLC+NxqN9neAiIiI3I7DTwlFR0ejvr4etbW12LJlCzZs2IALFy7Y7GM0GpGSkoLY2Fi88sork57r+++/x+uvv47jx49DEAS725CbmwudTmd9RUZGOtoNIiIiciO3XMOycuVKLFy4EO+99x4AoKurCwaDARqNBp9//jlUKtWkxx45cgQvvPACZLIbeZPJZIJMJkNkZCQaGhomPG7sCEtnZyeioqLQ1NTEGhYiIiI3YTQaERkZiY6ODuh0uin3nXYNi4XZbLYmD0ajEQaDAUqlEqdPn54yWQGA9PR0rFy50mabwWBAeno6Nm3aNOlxSqUSSqXS+t5yS4gjLURERO6nq6vLuQlLTk4OVq1ahaioKHR1daGoqAjV1dUoLy+H0WjEI488gt7eXpw4cQJGo9GaSISFhcHHxwcAsHjxYuTm5mLNmjUICQlBSEiIzXcoFArMnTsX0dHRdrcrPDwcTU1N0Gq1Dt1amg2W7JGjP67FOEgHYyENjIM0eHscRFFEV1eXXVOaOJSwtLS0YP369bh27Rp0Oh3i4uJQXl6O5ORkVFdXo7a2FgCwaNEim+OuXLmCBQsWAAAuXbrk0GRy9pDJZNDr9U49p7NZJtsj12IcpIOxkAbGQRq8OQ43G1mxcChhyc/Pn/SzBx54APaUw9xsn8nqVoiIiMh7cS0hIiIikjwmLDNMqVRiz549NkXCNPsYB+lgLKSBcZAGxsF+t/xYMxEREdFM4wgLERERSR4TFiIiIpI8JixEREQkeUxYiIiISPKYsDjotddew7333guNRoPAwMAJ98nOzsayZcugVCoRHx8/5fl+/fVXaLXaSc81Wl1dHR5++GEEBgYiKCgIBoMBP/74o+Od8ACuioNloc6JXi0tLdPrjBtz5fUAjMQjLi4OKpUKc+bMQVZWlmMd8CCujMVE18PHH3/seCc8gKuvCQBoa2uDXq+HIAjo6Oiw+zipY8LioMHBQTz55JPYsmXLlPtlZGRg7dq1U+4zNDSEdevW4b777rvp93Z3d+PRRx9FVFQUamtr8fXXX0Or1cJgMGBoaMihPngCV8Vh7dq1uHbtms3LYDAgKSkJc+bMcagPnsBVcQCAw4cPY9euXdi5cyd+/vlnnD17FgaDwe62expXxgIACgoKbK6Lxx57zO5jPYmr4wAAmZmZiIuLc+gYtyDStBQUFIg6nW7Kffbs2SMuXbp00s937NghpqWl2XWuuro6EYDY2Nho3fbTTz+JAMTLly870HLPMttxGKulpUVUKBRiYWGhQ8d5mtmOQ3t7u6hWq8WzZ8863lgP54prAoBYWlrqUDs9nat+m95++20xKSlJ/PLLL0UA4vXr1+1us9RxhMVFKisrUVxcjLfeesuu/aOjoxESEoL8/HwMDg6ir68P+fn5iImJsa7TRI5zNA5jFRYWQqPR4IknnnByy7yLo3GoqKiA2WxGc3MzYmJioNfr8dRTT6GpqWmGW+r5pntNZGVlITQ0FPfccw8++OADu5ZqoclNJw4XLlzA3r17UVhYCJnM8/68e16P3EBbWxs2btyI48eP273YlVarRXV1NU6cOAG1Wg1/f3+cOXMGZWVlkMsdWhKK/jadOIyVn5+P1NRUqNVqJ7fOe0wnDr/99hvMZjP279+PI0eOoKSkBO3t7UhOTsbg4OAMt9hzTfea2Lt3L06ePImKigo8/vjjeO6553D06NEZbKlnm04cBgYGsG7dOuTl5SEqKmqGW+gaTFgA7Ny5c9JCSsvrl19+cdr3PfPMM0hNTcX9999v9zF9fX3IzMxEYmIizp07h2+++QZLlixBSkoK+vr6nNY2V3KHOIxWU1ODixcvIjMz02ltkgJ3iIPZbMbQ0BDeeOMNGAwGrFixAh999BEuX76Mqqoqp7XN1dwhFgCwe/duJCYm4u6778bLL7+MHTt2IC8vz2ntcjV3iENOTg5iYmKQlpbmtHZIDf81B/Diiy9i48aNU+5z++23O+37Kisrcfr0aRw6dAjAyArWZrMZcrkcx44dQ0ZGxrhjioqK0NDQgJqaGutQX1FREYKCgnDq1Ck8/fTTTmufq7hDHEZ7//33ER8fj2XLljmtTVLgDnGYN28eACA2Nta6LSwsDKGhoWhsbHRa21zNHWIxkeXLl2Pfvn0YGBjwiDVy3CEOlZWVOH/+PEpKSqzHAEBoaCh27dqFV1991WntcxUmLBj5oQsLC5u176upqYHJZLK+P3XqFA4ePIhvv/0WEREREx7T29sLmUwGQRCs2yzvzWbzjLd5NrhDHCy6u7tx8uRJ5ObmznQzZ507xCExMREAcOnSJej1egBAe3s7WltbMX/+/Jlv9Cxxh1hMpL6+HkFBQR6RrADuEYdPP/3UZrS9rq4OGRkZ+Oqrr7Bw4cIZb/NsYMLioMbGRrS3t6OxsREmkwn19fUAgEWLFsHf3x/AyHPz3d3d+OOPP9DX12fdJzY2Fr6+voiJibE553fffQeZTIYlS5ZYt5WWliInJ8c6zJicnIzt27cjKysLW7duhdlsxoEDByCXy/Hggw/OfMclxlVxsPjkk08wPDzs0cOv9nBVHO68806sXr0azz//PI4dO4aAgADk5ORg8eLFXnk9AK6LxWeffYY///wTK1asgEqlQkVFBfbv34+XXnpp5jstQa6Kw9ikpLW1FQAQExPj0BwukubCJ5Tc0oYNG0QA415VVVXWfZKSkibc58qVKxOec6JH1goKCsSx4fniiy/ExMREUafTiUFBQeJDDz0k1tTUOLmH7sGVcRBFUUxISBBTU1Od2CP35Mo4dHZ2ihkZGWJgYKAYHBwsrlmzxuaxf2/jqliUlZWJ8fHxor+/v+jn5ycuXbpUfPfdd0WTyTQDvZQ+V/82WVRVVXncY82CKPLZMyIiIpI2PiVEREREkseEhYiIiCSPCQsRERFJHhMWIiIikjwmLERERCR5TFiIiIhI8piwEBERkeQxYSEiIiLJY8JCREREkseEhYiIiCSPCQsRERFJHhMWIiIikrz/A7ptaKaMt8gBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing squish no 2 for state AZ\n",
      "clipPoly 1 intersects [6, 8] counties and a total of 11 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 2 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Now back to polygonal HD drawing prep ... optional squish\")\n",
    "print(\"for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cbae976b-d97c-4dae-a4b5-a0b7eb4febe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes, so we can reset if needed\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]\n",
    "for t in range(nTracts):  #optional full state visualization after squish\n",
    "    if t not in allCutVTDs and t not in skipList:\n",
    "        plotPoly(vtdGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9715564f-3b60-43e5-9f1f-02f3dcee3933",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 22  #pick a county for visualization if desired\n",
    "print(\"here are the faint(original) and squished shapes for county\",c)\n",
    "for t in countyTractList[c]:\n",
    "    plotPoly(vtdGeom[t])\n",
    "    plotPoly(tractGeom[t],0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4ef91f8f-dabb-4c58-a8a4-bba8e725e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 0 is inside the map's convex hull\n",
      "working on confirming HD center 500 is inside the map's convex hull\n",
      "working on confirming HD center 1000 is inside the map's convex hull\n",
      "working on confirming HD center 1500 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD, MN, NC we draw HDs for ALL vtds, even if they are in a CCB.  We use the clipPoly's to squish in the corners for these HD shapes\n",
    "#Not sure if below would work if we have cut districts AND squished-in corners\n",
    "#We normally draw for each (squished or orig) vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]  #remember, this is after any squish operation\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[countyTractList[uncutCountyList[0]][0]].buffer(0.1) \n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList and v not in allCutVTDs:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:  #build the map via simple union of counties not wholly in fully cut-out districts\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8393e96a-75c6-48ec-94e8-1f6fe58d31df",
   "metadata": {},
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bcaf6ea2-1c3b-48c1-9ad6-937d7b7e4385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\n",
      "working on tract-tract distances for unit 0 out of 1538 . Time = 0\n",
      "working on tract-tract distances for unit 400 out of 1538 . Time = 26\n",
      "working on tract-tract distances for unit 800 out of 1538 . Time = 45\n",
      "working on tract-tract distances for unit 1200 out of 1538 . Time = 55\n",
      "All done; total elapsed  58 seconds for AZ with 9 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our maxD was 5.169104119375609\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT, despite the \"unit\" nomenclature\n",
    "print(\"Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default: too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on tract-tract distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()\n",
    "print(\"our maxD was\",maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2f392acb-ba52-4343-80ae-a58ba0b57366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 1538 . Time = 0\n",
      "working on unit-unit angles for unit 400 out of 1538 . Time = 14\n",
      "working on unit-unit angles for unit 800 out of 1538 . Time = 23\n",
      "working on unit-unit angles for unit 1200 out of 1538 . Time = 29\n",
      "All done with angles; total elapsed seconds = 30\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "#plt.hist(uuAngle)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4cc54eba-ca37-4680-9699-37332133afb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "794611.333 9 7151502.0 9.0 7151502.0\n",
      "7151502.0 7151502 1.0\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, statePop, statePop/aDP, trueStatePop)\n",
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), r5(np.sum(HDweight))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dac2348a-060a-40a9-beeb-02ff70ec1aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3873aaa2-0c39-4ac0-99c3-92dd7c18c555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 3719.897 of targetWedgePop\n",
      "I am working on HD number 0 of 1538 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 400 of 1538 HDs.  Elapsed sec = 62\n",
      "I am working on HD number 800 of 1538 HDs.  Elapsed sec = 124\n",
      "I am working on HD number 1200 of 1538 HDs.  Elapsed sec = 185\n",
      "236.831 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\n",
      "vtd avg and sd usage are 0.99996 0.16473\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b7f36a0f-a3c6-4b07-aecd-9e010b02918f",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATE = \"15Jul\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "37c238bb-b340-431b-ad11-95e4409d9699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea, \"countyNo\":countyNo,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_\"+DATE+\".csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "075013a3-f3ee-4a44-ac4a-27a7492e027a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./2024state_HD_output/AZ1538convexHD2_9nW4_15Jul.csv\n"
     ]
    }
   ],
   "source": [
    "print(\"./\"+outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/AZ1538convexHD2_9nW4_15Jul.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 --> 1 - we already have an ensemble of HDpoly's and their weights (from running option 2 above)\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"countyNo\" in shapeDF.columns.values:  #\"pigs\" == \"can fly\":\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "    if len(unassignedList) > 0:\n",
    "        if len(unassignedList) > 20:\n",
    "            print(\"  (too many to plot individually)\")\n",
    "        else:\n",
    "            print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "            for t in unassignedList:\n",
    "                print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "                plotPoly(hdCP[t].buffer(0.1))\n",
    "                plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "                plotPoly(countyGeom[HDcountyNo[t]])\n",
    "                plotPoly(MAP,0.2)\n",
    "                plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3f0f8434-5f7a-41d1-bd86-07ec230b4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, let me renormalize the HDweights if there were cut districts\n",
      "our HDweight sum was 0.9999999999999301 but is now 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"now, let me renormalize the HDweights if there were cut districts\")\n",
    "sumWt = np.sum(HDweight)\n",
    "HDweight = [HDweight[t]/sumWt for t in range(nHDs) ]\n",
    "\n",
    "print(\"our HDweight sum was\",sumWt,\"but is now\",np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af22eba0-8e05-43e7-b96b-6aedab016957",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see early states for continuing w/option 1.  NC and later use option 3 ....\n",
    "#if this is a cold restart, would need to read in unit topology before below block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8d5f678f-c2df-4ee7-bafe-fd75ba2cb42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\n"
     ]
    }
   ],
   "source": [
    "#OPTION 3 \n",
    "print(\"lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\")\n",
    "HDtractListString = shapeDF[\"HDtractList\"]\n",
    "HDtractList = [ast.literal_eval(HDtractListString[t]) for t in range(nHDs)]\n",
    "nCCBs = len(CCBlist)\n",
    "CCBpopFrac = [[0. for j in range(nCCBs) ]  for t in range(nHDs)]\n",
    "\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ec034144-b3dc-4b72-9350-6d85cabcb8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\n",
      "working on vtd --> unit conversion for HD 1 last HD had pop 783698\n",
      "working on vtd --> unit conversion for HD 1001 last HD had pop 791982.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\" )\n",
    "HDunitList = [ list() for t in range(nHDs) ]\n",
    "for t in range(nHDs):\n",
    "    if t %1000 == 1:\n",
    "        print(\"working on vtd --> unit conversion for HD\",t,\"last HD had pop\",r3( np.sum([unitPop[u] for u in HDunitList[t-1]]) ) )\n",
    "    capturedCountyPop = [0. for c in range(nCounties)]\n",
    "    capturedCCBpop = [0. for i in range(nCCBs)]\n",
    "    for v in HDtractList[t]:\n",
    "        c = HDcountyNo[v]\n",
    "        if c in range(nCounties):  #we assigned countyno = -999 to unpopulated tracts\n",
    "            if c in unitCounties:\n",
    "                capturedCountyPop[c] += tractPop[v]\n",
    "            elif c in allFusedCounties:\n",
    "                for i, L in enumerate(CCBlist):\n",
    "                    if c in L:\n",
    "                        capturedCCBpop[i] += tractPop[v]\n",
    "            else:  #this vtd not part of a unit or fused county.  Could be split into fragments\n",
    "                for u in countyUnitList[c]:\n",
    "                    if unitParentVTDno[u] == v :  #we assign all units = vtd fragments from this HDTL-captured vtd\n",
    "                        HDunitList[t].append(u)\n",
    "    for c in unitCounties:\n",
    "        if capturedCountyPop[c] > 0.5 * countyPop[c]:\n",
    "            HDunitList[t].append(allUnits.index(c+0.5))\n",
    "    for i in range(nCCBs):  #don't yet assign in/out CCBs.  We'll do so in below block.  Just update max possible use for each CCB\n",
    "        CCBpopFrac[t][i] = capturedCCBpop[i] / CCBpop[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1494814c-7b90-425c-b8b1-3a2006bdeb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\n",
      "CCBno 0 with pop 66021.0 would be used 2.166 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 1 with pop 16557.0 would be used 2.499 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 2 with pop 47669.0 would be used 1.485 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if we are not allowed to use multiple CCB's in a single HD 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCBno 0 with pop 66021.0 would be used 2.1664755180100186 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 1 with pop 16557.0 would be used 1.9378014576518414 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 2 with pop 47669.0 would be used 0.8078414856067937 if we added it to all adjoining districts with no 2-CCB districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\")\n",
    "CCBwouldUse = [0. for i in range(nCCBs)]\n",
    "CCBaddCandidates = [list() for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    ccbNbrSet = set(unitNbrs[jNo])\n",
    "    withCCBpopRat = list()\n",
    "    for t in range(nHDs):\n",
    "        if len(ccbNbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            CCBwouldUse[j] += HDweight[t] * nDistricts\n",
    "            CCBaddCandidates[j].append(t)\n",
    "            withCCBpopRat.append((np.sum([unitPop[u] for u in HDunitList[t]]) + CCBpop[j]) / aDP )\n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",r3(CCBwouldUse[j]),\"if we added it to all adjoining districts\")\n",
    "    print(\"Here is the histogram of relative district pop with these added\")\n",
    "    plt.hist(withCCBpopRat,histtype=\"step\",label=\"CCB \"+str(j))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "avoidMultiCCBuse = int(input(\"enter 1 if we are not allowed to use multiple CCB's in a single HD\"))  #option by state\n",
    "if avoidMultiCCBuse == 1:\n",
    "    for j in range(nCCBs):  #this block -- preventing HDs from picking up multiple CCB's; instead, pick up the closest one  \n",
    "        jNo = allUnits.index(j+0.25)\n",
    "        for jj in range(j+1, nCCBs):\n",
    "            jjNo = allUnits.index(j+0.25)\n",
    "            for t in CCBaddCandidates[j].copy():\n",
    "                if t in CCBaddCandidates[j]:\n",
    "                    if t in CCBaddCandidates[jj].copy():\n",
    "                        dist_j, dist_jj = hdCP[t].distance(unitCP[jNo]), hdCP[t].distance(unitCP[jjNo])\n",
    "                        if dist_j > dist_jj:\n",
    "                            CCBaddCandidates[j].remove(t)  #for MN, don't allow any districts with two CCB's\n",
    "                            CCBwouldUse[j] -= HDweight[t] * nDistricts\n",
    "                        else:\n",
    "                            CCBaddCandidates[jj].remove(t)\n",
    "                            CCBwouldUse[jj] -= HDweight[t] * nDistricts\n",
    "for j in range(nCCBs):    \n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",CCBwouldUse[j],\"if we added it to all adjoining districts with no 2-CCB districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0d38eb0e-b516-4fd4-bb9e-6ca0eecfa772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\n",
      "For PA, I recommend CCBuse of 1.03, 1.03, 1.01 to account for later shedding\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 0 I reco 1.02, as we may lose a little later 1.01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 0 is 1.01526 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 1 I reco 1.02, as we may lose a little later 1.01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 1 is 1.01651 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiEAAAGdCAYAAADE96MUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAApx0lEQVR4nO3de3RU1d3/8c/kNgnEmZAAM6QmEoQSRFBBDYN4KUZTfjzeSOvloS0iy0sbrRBvZD2CxUvDY61YLZfWhYBViuSpYqkKC6NFaUOUUBVEIyCatDCh1mZGLrmQ7N8fLqYMhMvMJG4G3q+1zpLZe8+Zb7YnzIcz5+xxGGOMAAAAvmEJtgsAAAAnJ0IIAACwghACAACsIIQAAAArCCEAAMAKQggAALCCEAIAAKwghAAAACuSbBdwsPb2dm3fvl2nnHKKHA6H7XIAAMAxMMboq6++UnZ2thISju0cx3EXQrZv366cnBzbZQAAgCjU19fr1FNPPaaxx10IOeWUUyR9/UO4XC7L1QAA0LE9Lft0/iOVkqR3/udSdUs57t5Sv1HBYFA5OTmh9/FjcdzN2P6PYFwuFyEEAHDcSmrZpwRnN0lfv2ed7CFkv0gupeDCVAAAYEVEIaStrU3Tpk1TXl6e0tLSdPrpp+uhhx7SgV/Ea4zR9OnT1adPH6WlpamwsFCbN2/u9MIBALApMcGh4mGnqnjYqUpM4EaKaER07uh///d/NXfuXC1atEiDBw/WunXrNHHiRLndbv30pz+VJD366KN68skntWjRIuXl5WnatGkqKirSpk2blJqa2iU/BAAA3zRnUqJ+ee1ZtsuIaw5z4GmMo/iv//oveTwezZ8/P9RWXFystLQ0PffcczLGKDs7W3fddZfuvvtuSVIgEJDH49HChQt1/fXXH/U1gsGg3G63AoEA14QAABAnonn/jujjmJEjR6qyslKffPKJJOn999/XmjVrNGbMGEnStm3b5Pf7VVhYGHqO2+1WQUGBqqqqOtxnc3OzgsFg2AYAwPHOGKM9Lfu0p2WfIvj3PA4Q0ccxU6dOVTAYVH5+vhITE9XW1qZHHnlE48ePlyT5/X5JksfjCXuex+MJ9R2svLxcM2bMiKZ2AACs2dvapjOmr5QkbXqwiLtjohDRmZClS5fq+eef1+LFi7V+/XotWrRIjz32mBYtWhR1AWVlZQoEAqGtvr4+6n0BAID4EVFsu+eeezR16tTQtR1DhgzR559/rvLyck2YMEFer1eS1NDQoD59+oSe19DQoLPPPrvDfTqdTjmdzijLBwAA8SqiMyF79uw5ZD34xMREtbe3S5Ly8vLk9XpVWVkZ6g8Gg6qurpbP5+uEcgEAwIkiojMhV1xxhR555BHl5uZq8ODB+tvf/qbHH39cN910k6SvV0mbPHmyHn74YQ0YMCB0i252drauvvrqrqgfAADEqYhCyFNPPaVp06bpJz/5iXbu3Kns7Gzdeuutmj59emjMvffeq927d+uWW25RY2OjRo0apRUrVrBGCAAACBPROiHfBNYJAQDEgz0t+7g75gDRvH+f3DMGAECUEhwO/b8h3tCfETlCCAAAUUhNTtSc8cNtlxHXCCEAgONG36mv2C4hYp/NHGu7hLgV0S26AAAAnYUQAgBADPpOfUV7WvbZLiMuEUIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBACAGHxnYC+WbY8SK6YCABCDBRPPt11C3OJMCAAAsIIQAgAArCCEAAAQg0HTVrBse5QIIQAAxGBva5vtEuIWIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAgBgV5mSzbHiWWbQcAIAYv3OqzXULc4kwIAACwghACAACsIIQAABCDYQ+tYtn2KBFCAACIwZe7W2yXELcIIQAAwApCCAAAsIIQAgAArCCEAAAAKwghAADACkIIAAAxGHqqm2XboxRRCOnbt68cDschW0lJiSSpqalJJSUlysrKUnp6uoqLi9XQ0NAlhQMAcDz44+2jlJqcaLuMuBRRCHn33Xe1Y8eO0LZq1SpJ0ve//31J0pQpU7R8+XJVVFRo9erV2r59u8aNG9f5VQMAgLgX0RfY9erVK+zxzJkzdfrpp+viiy9WIBDQ/PnztXjxYo0ePVqStGDBAg0aNEhr167ViBEjOq9qAAAQ96K+JqSlpUXPPfecbrrpJjkcDtXU1Ki1tVWFhYWhMfn5+crNzVVVVdVh99Pc3KxgMBi2AQAQLy6Y+Yb2trTZLiMuRR1Cli1bpsbGRt14442SJL/fr5SUFGVkZISN83g88vv9h91PeXm53G53aMvJyYm2JAAAvnH/aNwrI2O7jLgUdQiZP3++xowZo+zs7JgKKCsrUyAQCG319fUx7Q8AAMSHiK4J2e/zzz/X66+/rhdffDHU5vV61dLSosbGxrCzIQ0NDfJ6vYfdl9PplNPpjKYMAAAQx6I6E7JgwQL17t1bY8eODbUNHz5cycnJqqysDLXV1taqrq5OPp8v9koBAMAJJeIzIe3t7VqwYIEmTJigpKT/PN3tdmvSpEkqLS1VZmamXC6X7rjjDvl8Pu6MAQAAh4g4hLz++uuqq6vTTTfddEjfrFmzlJCQoOLiYjU3N6uoqEhz5szplEIBAMCJxWGMOa4u6Q0Gg3K73QoEAnK5XLbLAQB8g/pOfcV2CREb0Dtdf7x9lNJSTu5VU6N5/47qwlQAAPC1VaUX2y4hbvEFdgAAwApCCAAAsIIQAgBADC57fDXLtkeJEAIAQAw279zFsu1RIoQAAAArCCEAAMAKQggAALCCEAIAAKwghAAAACsIIQAAxOBbGWlyyGG7jLjEsu0AAMTgL1NH2y4hbnEmBAAAWEEIAQAAVhBCAACIwZW/XqOmVpZtjwYhBACAGHzw94DaDcu2R4MQAgAArCCEAAAAKwghAADACkIIAACwghACAACsIIQAABCDzO4ptkuIWyzbDgBADNZPu8x2CXGLMyEAAMAKQggAALCCEAIAQAyu+00Vy7ZHiRACAEAMqrd9ybLtUSKEAAAAKwghAADACkIIAACwghACAACsIIQAAAArCCEAAMQgLTnRdglxi2XbAQCIwUcPfdd2CXEr4jMh//jHP/SDH/xAWVlZSktL05AhQ7Ru3bpQvzFG06dPV58+fZSWlqbCwkJt3ry5U4sGAADxL6IQ8u9//1sXXHCBkpOT9dprr2nTpk365S9/qR49eoTGPProo3ryySc1b948VVdXq3v37ioqKlJTU1OnFw8AAOKXw5hjX+Zt6tSp+stf/qK33367w35jjLKzs3XXXXfp7rvvliQFAgF5PB4tXLhQ119//VFfIxgMyu12KxAIyOVyHWtpAIATQN+pr9guIWLfGdhLc38wXKkn+bUh0bx/R3Qm5I9//KPOPfdcff/731fv3r11zjnn6Omnnw71b9u2TX6/X4WFhaE2t9utgoICVVVVRfJSAADEhTdr/8my7VGKKIR8+umnmjt3rgYMGKCVK1fqxz/+sX76059q0aJFkiS/3y9J8ng8Yc/zeDyhvoM1NzcrGAyGbQAA4MQX0d0x7e3tOvfcc/Xzn/9cknTOOedo48aNmjdvniZMmBBVAeXl5ZoxY0ZUzwUAAPErojMhffr00RlnnBHWNmjQINXV1UmSvF6vJKmhoSFsTENDQ6jvYGVlZQoEAqGtvr4+kpIAAECciiiEXHDBBaqtrQ1r++STT3TaaadJkvLy8uT1elVZWRnqDwaDqq6uls/n63CfTqdTLpcrbAMAACe+iD6OmTJlikaOHKmf//znuvbaa/XOO+/ot7/9rX77299KkhwOhyZPnqyHH35YAwYMUF5enqZNm6bs7GxdffXVXVE/AACIUxGFkPPOO08vvfSSysrK9OCDDyovL09PPPGExo8fHxpz7733avfu3brlllvU2NioUaNGacWKFUpNTe304gEAQPyKaJ2QbwLrhADAySse1wn5bOZY2yUcF7p8nRAAAIDOQggBAABWEEIAAIjBT56vUVNrm+0y4hIhBACAGLy6wc+y7VEihAAAACsIIQAAwApCCAAAsIIQAgAArCCEAAAAKwghAADACkIIAAAx2PRgkdKSE22XEZci+gI7AAAQrlsKb6XR4kwIAACwghACAEAM7lr6vpr3sWx7NAghAADE4A/r/662dpZtjwYhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIhBzf2FLNseJdaaBQAgBlnpTtslxC3OhAAAACsIIQAAxGDaso0s2x4lQggAADH43drPWbY9SoQQAABgBSEEAABYQQgBAABWEEIAAIAVrBMCAECMzpi+0nYJEfls5ljbJUjiTAgAALCEEAIAAKwghAAAACsiCiE/+9nP5HA4wrb8/PxQf1NTk0pKSpSVlaX09HQVFxeroaGh04sGAADxL+IzIYMHD9aOHTtC25o1a0J9U6ZM0fLly1VRUaHVq1dr+/btGjduXKcWDAAATgwR3x2TlJQkr9d7SHsgEND8+fO1ePFijR49WpK0YMECDRo0SGvXrtWIESNirxYAAJwwIj4TsnnzZmVnZ6tfv34aP3686urqJEk1NTVqbW1VYWFhaGx+fr5yc3NVVVXVeRUDAIATQkRnQgoKCrRw4UINHDhQO3bs0IwZM3ThhRdq48aN8vv9SklJUUZGRthzPB6P/H7/YffZ3Nys5ubm0ONgMBjZTwAAAOJSRCFkzJgxoT8PHTpUBQUFOu2007R06VKlpaVFVUB5eblmzJgR1XMBAED8iukW3YyMDH3729/Wli1b5PV61dLSosbGxrAxDQ0NHV5Dsl9ZWZkCgUBoq6+vj6UkAAAQJ2IKIbt27dLWrVvVp08fDR8+XMnJyaqsrAz119bWqq6uTj6f77D7cDqdcrlcYRsAADjxRfRxzN13360rrrhCp512mrZv364HHnhAiYmJuuGGG+R2uzVp0iSVlpYqMzNTLpdLd9xxh3w+H3fGAACAQ0QUQv7+97/rhhtu0L/+9S/16tVLo0aN0tq1a9WrVy9J0qxZs5SQkKDi4mI1NzerqKhIc+bM6ZLCAQBAfHMYY4ztIg4UDAbldrsVCAT4aAYATjJ9p75iu4STQld8i2407998dwwAALCCEAIAAKwghAAAACsIIQAAwApCCAAAsIIQAgAArCCEAAAAKwghAADACkIIAACwghACAACsIIQAAAArCCEAAMAKQggAALCCEAIAAKwghAAAACsIIQAAwApCCAAAsIIQAgAArCCEAAAAKwghAADACkIIAACwghACAACsIIQAAAArCCEAAMAKQggAALCCEAIAAKwghAAAACsIIQAAwApCCAAAsIIQAgAArCCEAAAAKwghAADACkIIAACwghACAACsIIQAAAArYgohM2fOlMPh0OTJk0NtTU1NKikpUVZWltLT01VcXKyGhoZY6wQAACeYqEPIu+++q9/85jcaOnRoWPuUKVO0fPlyVVRUaPXq1dq+fbvGjRsXc6EAAODEElUI2bVrl8aPH6+nn35aPXr0CLUHAgHNnz9fjz/+uEaPHq3hw4drwYIF+utf/6q1a9d2WtEAACD+RRVCSkpKNHbsWBUWFoa119TUqLW1Naw9Pz9fubm5qqqq6nBfzc3NCgaDYRsAADjxJUX6hCVLlmj9+vV69913D+nz+/1KSUlRRkZGWLvH45Hf7+9wf+Xl5ZoxY0akZQAAgDgX0ZmQ+vp63XnnnXr++eeVmpraKQWUlZUpEAiEtvr6+k7ZLwAAOL5FFEJqamq0c+dODRs2TElJSUpKStLq1av15JNPKikpSR6PRy0tLWpsbAx7XkNDg7xeb4f7dDqdcrlcYRsAADjxRfRxzKWXXqoNGzaEtU2cOFH5+fm67777lJOTo+TkZFVWVqq4uFiSVFtbq7q6Ovl8vs6rGgAAxL2IQsgpp5yiM888M6yte/fuysrKCrVPmjRJpaWlyszMlMvl0h133CGfz6cRI0Z0XtUAACDuRXxh6tHMmjVLCQkJKi4uVnNzs4qKijRnzpzOfhkAABDnHMYYY7uIAwWDQbndbgUCAa4PAYCTTN+pr9gu4aTw2cyxnb7PaN6/+e4YAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWBFRCJk7d66GDh0ql8sll8sln8+n1157LdTf1NSkkpISZWVlKT09XcXFxWpoaOj0ogEAQPyLKISceuqpmjlzpmpqarRu3TqNHj1aV111lT788ENJ0pQpU7R8+XJVVFRo9erV2r59u8aNG9clhQMAgPjmMMaYWHaQmZmpX/ziF/re976nXr16afHixfre974nSfr44481aNAgVVVVacSIEce0v2AwKLfbrUAgIJfLFUtpAIA403fqK7ZLOCl8NnNsp+8zmvfvqK8JaWtr05IlS7R79275fD7V1NSotbVVhYWFoTH5+fnKzc1VVVXVYffT3NysYDAYtgEAgBNfxCFkw4YNSk9Pl9Pp1G233aaXXnpJZ5xxhvx+v1JSUpSRkRE23uPxyO/3H3Z/5eXlcrvdoS0nJyfiHwIAAMSfiEPIwIED9d5776m6ulo//vGPNWHCBG3atCnqAsrKyhQIBEJbfX191PsCAADxIynSJ6SkpKh///6SpOHDh+vdd9/Vr371K1133XVqaWlRY2Nj2NmQhoYGeb3ew+7P6XTK6XRGXjkAAIhrMa8T0t7erubmZg0fPlzJycmqrKwM9dXW1qqurk4+ny/WlwEAACeYiM6ElJWVacyYMcrNzdVXX32lxYsX689//rNWrlwpt9utSZMmqbS0VJmZmXK5XLrjjjvk8/mO+c4YAABw8ogohOzcuVM/+tGPtGPHDrndbg0dOlQrV67UZZddJkmaNWuWEhISVFxcrObmZhUVFWnOnDldUjgAAIhvMa8T0tlYJwQATl6sE/LNiPt1QgAAAGJBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWRBRCysvLdd555+mUU05R7969dfXVV6u2tjZsTFNTk0pKSpSVlaX09HQVFxeroaGhU4sGAADxL6IQsnr1apWUlGjt2rVatWqVWltbdfnll2v37t2hMVOmTNHy5ctVUVGh1atXa/v27Ro3blynFw4AAOJbUiSDV6xYEfZ44cKF6t27t2pqanTRRRcpEAho/vz5Wrx4sUaPHi1JWrBggQYNGqS1a9dqxIgRnVc5AACIazFdExIIBCRJmZmZkqSamhq1traqsLAwNCY/P1+5ubmqqqrqcB/Nzc0KBoNhGwAAOPFFHULa29s1efJkXXDBBTrzzDMlSX6/XykpKcrIyAgb6/F45Pf7O9xPeXm53G53aMvJyYm2JAAAEEeiDiElJSXauHGjlixZElMBZWVlCgQCoa2+vj6m/QEAgPgQ0TUh+91+++3605/+pLfeekunnnpqqN3r9aqlpUWNjY1hZ0MaGhrk9Xo73JfT6ZTT6YymDAAAEMciOhNijNHtt9+ul156SW+88Yby8vLC+ocPH67k5GRVVlaG2mpra1VXVyefz9c5FQMAgBNCRGdCSkpKtHjxYr388ss65ZRTQtd5uN1upaWlye12a9KkSSotLVVmZqZcLpfuuOMO+Xw+7owBAABhIgohc+fOlSRdcsklYe0LFizQjTfeKEmaNWuWEhISVFxcrObmZhUVFWnOnDmdUiwAADhxRBRCjDFHHZOamqrZs2dr9uzZURcFAABOfHx3DAAAsIIQAgAArCCEAAAAKwghAADACkIIAACwghACAACsIIQAAAArovruGOBE1HfqK7ZLiNhnM8faLgEAosaZEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGBFku0CcGLqO/UV2yUAAI5znAkBAABWEEIAAIAVhBAAAGAFIQQAAFgRcQh56623dMUVVyg7O1sOh0PLli0L6zfGaPr06erTp4/S0tJUWFiozZs3d1a9AADgBBFxCNm9e7fOOusszZ49u8P+Rx99VE8++aTmzZun6upqde/eXUVFRWpqaoq5WAAAcOKI+BbdMWPGaMyYMR32GWP0xBNP6P7779dVV10lSXr22Wfl8Xi0bNkyXX/99bFVCwAAThidek3Itm3b5Pf7VVhYGGpzu90qKChQVVVVh89pbm5WMBgM2wAAwImvUxcr8/v9kiSPxxPW7vF4Qn0HKy8v14wZMzqzjCOKx0W0Pps51nYJAAB0Out3x5SVlSkQCIS2+vp62yUBAIBvQKeGEK/XK0lqaGgIa29oaAj1HczpdMrlcoVtAADgxNepISQvL09er1eVlZWhtmAwqOrqavl8vs58KQAAEOciviZk165d2rJlS+jxtm3b9N577ykzM1O5ubmaPHmyHn74YQ0YMEB5eXmaNm2asrOzdfXVV3dm3QAAIM5FHELWrVun73znO6HHpaWlkqQJEyZo4cKFuvfee7V7927dcsstamxs1KhRo7RixQqlpqZ2XtUAACDuRRxCLrnkEhljDtvvcDj04IMP6sEHH4ypMAAAcGKzfncMAAA4ORFCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYAUhBAAAWEEIAQAAViTZLgBH13fqK7ZLwHEqHo+Nz2aOtV1CxOJxnqX4nGucXDgTAgAArCCEAAAAKwghAADACq4JAfCNitfrKwB0Ps6EAAAAKwghAADACkIIAACwghACAACs6LIQMnv2bPXt21epqakqKCjQO++801UvBQAA4lCXhJAXXnhBpaWleuCBB7R+/XqdddZZKioq0s6dO7vi5QAAQBzqkhDy+OOP6+abb9bEiRN1xhlnaN68eerWrZueeeaZrng5AAAQhzp9nZCWlhbV1NSorKws1JaQkKDCwkJVVVUdMr65uVnNzc2hx4FAQJIUDAY7uzRJUnvzni7ZLwAcb7rq79GuxN/R34yuODb279MYc8zP6fQQ8sUXX6itrU0ejyes3ePx6OOPPz5kfHl5uWbMmHFIe05OTmeXBgAnFfcTtivA8aorj42vvvpKbrf7mMZaXzG1rKxMpaWlocft7e368ssvlZWVJYfDYbEy+4LBoHJyclRfXy+Xy2W7nLjEHMaOOYwdcxg75jB2XT2Hxhh99dVXys7OPubndHoI6dmzpxITE9XQ0BDW3tDQIK/Xe8h4p9Mpp9MZ1paRkdHZZcU1l8vFL12MmMPYMYexYw5jxxzGrivn8FjPgOzX6RempqSkaPjw4aqsrAy1tbe3q7KyUj6fr7NfDgAAxKku+TimtLRUEyZM0Lnnnqvzzz9fTzzxhHbv3q2JEyd2xcsBAIA41CUh5LrrrtM///lPTZ8+XX6/X2effbZWrFhxyMWqODKn06kHHnjgkI+rcOyYw9gxh7FjDmPHHMbueJxDh4nkXhoAAIBOwnfHAAAAKwghAADACkIIAACwghACAACsIITEoG/fvnI4HIdsJSUlkqRLLrnkkL7bbrstbB91dXUaO3asunXrpt69e+uee+7Rvn37wsb8+c9/1rBhw+R0OtW/f38tXLjwkFpmz56tvn37KjU1VQUFBXrnnXfC+puamlRSUqKsrCylp6eruLj4kAXlbDjaHEpSVVWVRo8ere7du8vlcumiiy7S3r17Q/1ffvmlxo8fL5fLpYyMDE2aNEm7du0Ke50PPvhAF154oVJTU5WTk6NHH330kFoqKiqUn5+v1NRUDRkyRK+++mpYvzFG06dPV58+fZSWlqbCwkJt3ry5k2ckckeaw88++6zDPofDoYqKitA+OA6PfBz6/X798Ic/lNfrVffu3TVs2DD94Q9/CNsHx+GR53Dr1q265ppr1KtXL7lcLl177bWH/L8/2eewra1N06ZNU15entLS0nT66afroYceCvsulmOpPa7m0SBqO3fuNDt27Ahtq1atMpLMm2++aYwx5uKLLzY333xz2JhAIBB6/r59+8yZZ55pCgsLzd/+9jfz6quvmp49e5qysrLQmE8//dR069bNlJaWmk2bNpmnnnrKJCYmmhUrVoTGLFmyxKSkpJhnnnnGfPjhh+bmm282GRkZpqGhITTmtttuMzk5OaaystKsW7fOjBgxwowcObLrJ+kojjaHf/3rX43L5TLl5eVm48aN5uOPPzYvvPCCaWpqCu3ju9/9rjnrrLPM2rVrzdtvv2369+9vbrjhhlB/IBAwHo/HjB8/3mzcuNH8/ve/N2lpaeY3v/lNaMxf/vIXk5iYaB599FGzadMmc//995vk5GSzYcOG0JiZM2cat9ttli1bZt5//31z5ZVXmry8PLN3796un6gjONIc7tu3L6xvx44dZsaMGSY9Pd189dVXxhiOQ2OOfhxedtll5rzzzjPV1dVm69at5qGHHjIJCQlm/fr1oX1wHB5+Dnft2mX69etnrrnmGvPBBx+YDz74wFx11VXmvPPOM21tbaF9nOxz+Mgjj5isrCzzpz/9yWzbts1UVFSY9PR086tf/So05lhqj6d5JIR0ojvvvNOcfvrppr293RjzdQi58847Dzv+1VdfNQkJCcbv94fa5s6da1wul2lubjbGGHPvvfeawYMHhz3vuuuuM0VFRaHH559/vikpKQk9bmtrM9nZ2aa8vNwYY0xjY6NJTk42FRUVoTEfffSRkWSqqqqi/4G7wMFzWFBQYO6///7Djt+0aZORZN59991Q22uvvWYcDof5xz/+YYwxZs6cOaZHjx6hOTXGmPvuu88MHDgw9Pjaa681Y8eODdt3QUGBufXWW40xxrS3txuv12t+8YtfhPobGxuN0+k0v//972P4iTvfwXN4sLPPPtvcdNNNoccch4c6eA67d+9unn322bAxmZmZ5umnnzbGcBx25MA5XLlypUlISAj7R1hjY6NxOBxm1apVxhjm0Bhjxo4dG/a7aYwx48aNM+PHjzfGHFvt8TaPfBzTSVpaWvTcc8/ppptuCvviveeff149e/bUmWeeqbKyMu3Z85+vqa6qqtKQIUPCFnErKipSMBjUhx9+GBpTWFgY9lpFRUWqqqoKvW5NTU3YmISEBBUWFobG1NTUqLW1NWxMfn6+cnNzQ2OOBwfP4c6dO1VdXa3evXtr5MiR8ng8uvjii7VmzZrQc6qqqpSRkaFzzz031FZYWKiEhARVV1eHxlx00UVKSUkJjSkqKlJtba3+/e9/h8YcaZ63bdsmv98fNsbtdqugoOC4nsOD1dTU6L333tOkSZNCbRyH4Tqaw5EjR+qFF17Ql19+qfb2di1ZskRNTU265JJLJHEcHuzgOWxubpbD4QhbJCs1NVUJCQmh32fm8OvjrLKyUp988okk6f3339eaNWs0ZswYScdWe7zNIyGkkyxbtkyNjY268cYbQ23//d//reeee05vvvmmysrK9Lvf/U4/+MEPQv1+v/+QVWT3P/b7/UccEwwGtXfvXn3xxRdqa2vrcMyB+0hJSTnkiwEPHHM8OHgOP/30U0nSz372M918881asWKFhg0bpksvvTT0uaPf71fv3r3D9pOUlKTMzMyjzuH+viONObD/wOd1NOZ40NFxeKD58+dr0KBBGjlyZKiN4zBcR3O4dOlStba2KisrS06nU7feeqteeukl9e/fXxLH4cEOnsMRI0aoe/fuuu+++7Rnzx7t3r1bd999t9ra2rRjxw5JzKEkTZ06Vddff73y8/OVnJysc845R5MnT9b48eMlHVvt8TaPhJBOMn/+fI0ZMybsK4xvueUWFRUVaciQIRo/fryeffZZvfTSS9q6davFSo9fB89he3u7JOnWW2/VxIkTdc4552jWrFkaOHCgnnnmGZulHrc6Og7327t3rxYvXhx2FgSH6mgOp02bpsbGRr3++utat26dSktLde2112rDhg0WKz1+HTyHvXr1UkVFhZYvX6709HS53W41NjZq2LBhSkjgbWi/pUuX6vnnn9fixYu1fv16LVq0SI899pgWLVpku7Qu0yXfHXOy+fzzz/X666/rxRdfPOK4goICSdKWLVt0+umny+v1HnL3wP6rxb1eb+i/B19B3tDQIJfLpbS0NCUmJioxMbHDMQfuo6WlRY2NjWH/Cj1wjG0dzWGfPn0kSWeccUbY2EGDBqmurk7S1z/bzp07w/r37dunL7/88qhzuL/vSGMO7N/ftr+u/Y/PPvvsyH/gLnC04/D//u//tGfPHv3oRz8Ka+c4/I+O5nDr1q369a9/rY0bN2rw4MGSpLPOOktvv/22Zs+erXnz5nEcHuBwx+Hll1+urVu36osvvlBSUpIyMjLk9XrVr18/SfwuS9I999wTOhsiSUOGDNHnn3+u8vJyTZgw4Zhqj7d5JIJ2ggULFqh3794aO3bsEce99957kv7z5urz+bRhw4awA2bVqlVyuVyhN16fz6fKysqw/axatUo+n0+SlJKSouHDh4eNaW9vV2VlZWjM8OHDlZycHDamtrZWdXV1oTG2dTSHffv2VXZ2tmpra8PGfvLJJzrttNMkfT0/jY2NqqmpCfW/8cYbam9vD4U+n8+nt956S62traExq1at0sCBA9WjR4/QmCPNc15enrxeb9iYYDCo6urq43oODzR//nxdeeWV6tWrV1g7x+F/dDSH+6/jOvhf7ImJiaGzdRyH/3G047Bnz57KyMjQG2+8oZ07d+rKK6+UxBxKXx9rRzrOjqX2uJvHY76EFR1qa2szubm55r777gtr37Jli3nwwQfNunXrzLZt28zLL79s+vXrZy666KLQmP23Rl5++eXmvffeMytWrDC9evXq8NbIe+65x3z00Udm9uzZHd4a6XQ6zcKFC82mTZvMLbfcYjIyMsLudrjttttMbm6ueeONN8y6deuMz+czPp+vC2fm2B1uDo0xZtasWcblcpmKigqzefNmc//995vU1FSzZcuW0Jjvfve75pxzzjHV1dVmzZo1ZsCAAWG3ozU2NhqPx2N++MMfmo0bN5olS5aYbt26HXI7WlJSknnsscfMRx99ZB544IEOb0fLyMgwL7/8cugWw+Phtj5jjjyHxhizefNm43A4zGuvvXZIH8fh1w43hy0tLaZ///7mwgsvNNXV1WbLli3mscceMw6Hw7zyyiuhcRyHRz4On3nmGVNVVWW2bNlifve735nMzExTWloaNuZkn8MJEyaYb33rW6FbdF988UXTs2dPc++994bGHEvt8TSPhJAYrVy50kgytbW1Ye11dXXmoosuMpmZmcbpdJr+/fube+65J+wWNWOM+eyzz8yYMWNMWlqa6dmzp7nrrrtMa2tr2Jg333zTnH322SYlJcX069fPLFiw4JA6nnrqKZObm2tSUlLM+eefb9auXRvWv3fvXvOTn/zE9OjRw3Tr1s1cc801ZseOHZ0zCTE63BzuV15ebk499VTTrVs34/P5zNtvvx3W/69//cvccMMNJj093bhcLjNx4sTQGhj7vf/++2bUqFHG6XSab33rW2bmzJmHvM7SpUvNt7/9bZOSkmIGDx4c9gZjzNe3pE2bNs14PB7jdDrNpZdeetiav2lHm8OysjKTk5MTtibDgTgOjzyHn3zyiRk3bpzp3bu36datmxk6dOght+xyHB55Du+77z7j8XhMcnKyGTBggPnlL395yG3kJ/scBoNBc+edd5rc3FyTmppq+vXrZ/7nf/4n7FbaY6k9nubRYcwBS7EBAAB8Q7gmBAAAWEEIAQAAVhBCAACAFYQQAABgBSEEAABYQQgBAABWEEIAAIAVhBAAAGAFIQQAAFhBCAEAAFYQQgAAgBWEEAAAYMX/B2eB1X56d0+7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 2 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 2 is 0.80784 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\")\n",
    "print(\"For PA, I recommend CCBuse of 1.03, 1.03, 1.01 to account for later shedding\")\n",
    "#previously, we went from most-used to least-used CCB vtds until each has unit use\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "CCBuse = [0. for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    minCCBuse = float(input(\"enter target CCBuse for CCB \"+str(j)+\" I reco 1.02, as we may lose a little later\"))\n",
    "    postCCBaddPops = list()\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    #ccbNbrSet = set(unitNbrs[unitNo])\n",
    "    #ccbFrac = [-1.*CCBpopFrac[t][j] for t in range(nHDs) ]  # -1 to go from most- to least-used\n",
    "    #idx = np.argsort(ccbFrac)\n",
    "    preCCBpop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in CCBaddCandidates[j]]\n",
    "    idx = np.argsort(preCCBpop)\n",
    "    i = 0\n",
    "    t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "    while CCBuse[j] < minCCBuse  and i < len(CCBaddCandidates[j]) :  #and CCBpopFrac[t][j] > 0:\n",
    "        t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        HDunitList[t].append(unitNo)\n",
    "        postCCBaddPops.append(np.sum([unitPop[u] for u in HDunitList[t] ]))\n",
    "        i +=1\n",
    "    unitUse[unitNo] = CCBuse[j]\n",
    "    print(\"final unit use of CCB\",j, \"is\",r5(CCBuse[j]),\"; here is a histogram of HDpop using this CCB\")\n",
    "    plt.hist(postCCBaddPops)\n",
    "    plt.axvline(aDP,ls=\"--\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf747d9c-cf6d-420e-9c3c-6723619586ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end of preliminaries for \"option 3\" - converting HDtractlists to unit lists instead of option 2's geometric probing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26102ca2-79c1-48db-b873-7fa3a1553911",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 0.99827 0.16427\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs) ]\n",
    "print(\"Here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t] / aDP for t in range(nHDs) ], bins=20, weights = HDweight, label= \"HDpop / target\" )\n",
    "plt.show()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e4c298-e6cb-4fee-a511-b8f6c3940539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c4183011-724a-4014-8c94-f9f9382c7e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did I grossly overuse any units, or skip any live ones?\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0cUlEQVR4nO3dfXSU9Z3//9c195nMTe7vSMJNgECAiNJqg3fVCharpd/T325PbRdq3bZ6era7ttubnO2utT2utLSr7h7L0aote7acdnHrrtuqWV0F20VRFOROQBDkLpBAwszkbjKZuX5/gIEAwUwymWtm8nyckz+umSvXvPMBcr34XJ8bwzRNUwAAAGlis7oAAAAwsRA+AABAWhE+AABAWhE+AABAWhE+AABAWhE+AABAWhE+AABAWhE+AABAWjmsLuB8iURCR48eld/vl2EYVpcDAABGwDRNRSIRVVVVyWa7dN9GxoWPo0ePqqamxuoyAADAKBw6dEjV1dWXPCfjwoff75d0uvhAIGBxNQAAYCTC4bBqamoG7+OXknHh44NHLYFAgPABAECWGcmQCQacAgCAtCJ8AACAtCJ8AACAtCJ8AACAtCJ8AACAtCJ8AACAtCJ8AACAtCJ8AACAtCJ8AACAtCJ8AACAtEoqfKxatUqNjY2DS583NTXpueeekyQdOHBAhmFc9Gvt2rXjUjwAAMg+Se3tUl1drRUrVmjGjBkyTVOrV6/W0qVLtXnzZs2aNUutra1Dzn/ssce0cuVKLVmyJKVFAwCA7GWYpmmO5QJFRUVauXKl7rzzzgveu/zyy3XFFVfoiSeeGPH1wuGwgsGgQqEQG8sBAJAlkrl/j3pX23g8rrVr16q7u1tNTU0XvP/mm29qy5YteuSRRy55nWg0qmg0OngcDodHWxLOs/9Etw529FhdRkp9kJVHsmsigPGRyf8OO7v7VZjvsrqMjJZImLphVpmlNSQdPrZt26ampib19fXJ5/Pp6aefVkNDwwXnPfHEE5o9e7YWLlx4yes98MADuu+++5ItAyNwsKNH188stboMAEAGWb+n3eoSkp/tUl9fry1btmjjxo26++67tXz5cu3cuXPIOb29vVqzZs1FH8Wcr7m5WaFQaPDr0KFDyZYEAACySNI9Hy6XS9OnT5ckLViwQG+88YYefvhhPfroo4PnPPXUU+rp6dGyZcs+9Hput1tutzvZMgAAQJYa8zofiURiyJgN6fQjl09/+tMqLaXLHwAADJVUz0dzc7OWLFmi2tpaRSIRrVmzRuvWrVNLS8vgOXv37tUrr7yiZ599NuXFAgCA7JdU+Ghra9OyZcvU2tqqYDCoxsZGtbS0aNGiRYPnPPnkk6qurtbixYtTXiwAAMh+Y17nI9VY5yN11u9pZ7YLAGCI8bo3JHP/Zm8XAACQVoQPAACQVoSPHJZhT9QAAJBE+Mhpmbj0MQAAhA8AAJBWhA8AAJBWhI8cxpgPAEAmInzkMMZ8AAAyEeEDAACkFeEDAACkFeEDAACkFeEjh9kMKZ5g0CkAILMQPnKYw2ZTLJ6wugwAAIYgfOQwp93QAD0fAIAMQ/jIYQ67TQP0fAAAMgzhI4c5bIZicXo+AACZhfCRw9wOm/rp+QAAZBjCRw5zOWzqHyB8AAAyC+Ejh7kddsIHACDjED5ymMthU3QgbnUZAAAMQfjIYW4euwAAMhDhI4e5HTb1xQgfAIDMQvjIYQ67TXGTqbYAgMxC+MhxJuEDAJBhCB85zjAMq0sAAGAIwgcAAEgrwgcAAEgrwgcAAEgrwkeOY8ApACDTED5yHANOAQCZhvABAADSivABAADSivABAADSivABAADSivABAADSivABAADSivABAADSivABAADSivABAADSivABAADSymF1ARgfXdEBbdh7QnVlPqtLAQBgCMJHDuqLxfXSrjbdOq9SNht7uwAAMgvhY5SiA3Ed7uw9s2usoZqiPHX1Dago32X5Zm7r97TrlrkVBA8AQEYifIzA4c4ebTl0SgvrSnTgZLdO9fTLabdpSnG+DEMyTenNA51yOmxq2XFct19Vq/fau3TgZLcaqwsUzHPqT3tPKJEwZbcZMiX1DyR0RW2hSv3ulNb6+v4Oza4IyGFnOA8AIDMRPi7hyKlebT7YqbpSnz41r1Kv7+/QtFKfrqgtvODcmiKvOrr7daonppd3tam22KsbZ5XrT++eUCyeUFNdsTxO+5DveWVPu/a123TV1KIx95b09se19fApRQcSqi32julaAACMJ8M8/dwgY4TDYQWDQYVCIQUCgbR8ZqQvpk0HOoc8pogNJDSpME/15f5xfXxxsiuqDftO6vLaAlUXjj40PPGn/fr8lTXyusiTAIDhrd/Trutnlqb8usncvyf8nWrP8YhaQ336eH2pJWM1in1u3XZZld58v0Mnuvo1v6ZgVNfxue3yOOwffiIAABab8AMDSn1u9cXilg8SXTC5SO+1d2k0HVGbD3bqqqnFDDAFAGSFpMLHqlWr1NjYqEAgoEAgoKamJj333HNDznn11Vd14403Kj8/X4FAQNddd516e3tTWnQqFea7VF2Ypx1HQ5bWYZqmogMJ/e87bYr0xUb8fX2xuE71xjSlJH8cqwMAIHWSeuxSXV2tFStWaMaMGTJNU6tXr9bSpUu1efNmzZkzR6+++qo++clPqrm5Wf/yL/8ih8Oht99+WzZbZnewnOjql82QBuIJy2aJGIahz19Zq4F4Qm8dPKWDHT36/xZUf+j3bTl0SlOKCR4AgOwx5gGnRUVFWrlype6880597GMf06JFi/SjH/1o1NezYsDpQDyhF985rrpSn2aU+9PymR/mZFdUr+/v0NTSfE0r8amnf0CHO3vV3hWVTi8tIplSRdCj2ZXpaScAQPbL6gGn8Xhca9euVXd3t5qamtTW1qaNGzfqC1/4ghYuXKh9+/Zp1qxZuv/++3XNNdcMe51oNKpoNDqk+HRbt7tdH68vu2AqrJWKfW4tmVepQx09euNAh7wuuyYV5mlOVcDy8SkAAIxF0s8Ytm3bJp/PJ7fbrbvuuktPP/20Ghoa9N5770mSfvCDH+grX/mKnn/+eV1xxRX6xCc+oXfffXfY6z3wwAMKBoODXzU1NaP/aUZpIGHK7cjMR0M1RV5dPb1El9cWqszvIXgAALJe0nfc+vp6bdmyRRs3btTdd9+t5cuXa+fOnUokEpKkr33ta7rjjjt0+eWX68EHH1R9fb2efPLJYa/X3NysUCg0+HXo0KHR/zSjVFOUp52t6e9xAQBgIkr6sYvL5dL06dMlSQsWLNAbb7yhhx9+WN/73vckSQ0NDUPOnz17tg4ePDjs9dxut9zu1C4xnqw5VUGt292mtnCfygIeS2sBACDXjflZQyKRUDQa1ZQpU1RVVaXdu3cPeX/Pnj2aPHnyWD9m3H28vkyb3u9UV3TA6lIAAMhpSfV8NDc3a8mSJaqtrVUkEtGaNWu0bt06tbS0yDAMffvb39a9996ryy67TPPnz9fq1au1a9cuPfXUU+NVf0otmVuhZ7cd06caK60uBQCAnJVU+Ghra9OyZcvU2tqqYDCoxsZGtbS0aNGiRZKkv/mbv1FfX5/uuecedXR06LLLLtMLL7ygurq6cSk+1QzDUL7brgMnulm0CwCAccLGchex/UhIR0716sZZZXKyNT0AIIdkwjof3FkvYu6koC6vLdA/PvuO+gcSVpcDAEBOmfC72p5v6+FTOhbqU4nfrb+7ZbZly60DAJCrCB/nSZjS5OJ81VdkxjLrAADkGv5bfx6HzZDDziqiAACMF8LHOQbiCb19+JTqSn1WlwIAQM4ifJzDYbeprtSnju5+q0sBACBnET7Ok+9yKBZnhgsAAOOF8HGeuZMC2nyw0+oyAADIWYSP80QHEsp3MwkIAIDxQvg4z8b9HbpyapHVZQAAkLMIH+fxuuzac6zL6jIAAMhZhI/zfHRKkQrznXp5V5vVpQAAkJMIHxfRHY2rujDP6jIAAMhJhI+LiPTF5HLQNAAAjAfusBfxkSlFOtEV1e5jEatLAQAg5xA+hrFgcpF6+gf05vsdVpcCAEBOIXxcwuW1hTrVE7O6DAAAcgqraQ2jo7tff3y3XbMrA1aXAgBATqHnYxgFeU5J0sxyv8WVAACQWwgfw7DZDFUXehXisQsAAClF+LiEOVUBvX6AAacAAKQSYz6Gsf9Et/Ycj+j6maVWlwIAQE4hfAzjvfYuLW4ol2EYVpcCAEBO4bHLMOZOCmrr4ZDVZQAAkHMIH8Mo87t14GS31WUAAJBzCB/DMAxDNUVeHTnVa3UpAADkFMLHJVxRW6hth0Nqi/RZXQoAADmD8PEhPjm3Qu+1d2vDvhNWlwIAQE4gfIzAx6YVq7rAq51Hw1aXAgBA1iN8jFBtsVcHO3qsLgMAgKxH+EhCQ2VA248w/RYAgLEgfCShttir1hCDTwEAGAvCR5Kmlnh14ATrfwAAMFqEjyRVBvPU3hW1ugwAALIW4SNJ0YGEHDb2ewEAYLQIH0kqynepo7vf6jIAAMhahI9RsBmGTNO0ugwAALIS4WMUin0unaT3AwCAUSF8jEJDZUA7WO0UAIBRIXyMgsNuU18sbnUZAABkJcLHKE0qyNPBkyy3DgBAsggfozS7MqADJ1lsDACAZBE+RulUT7+CeU6rywAAIOsQPkbpndaIZlX6rS4DAICs47C6gGwRiyf0/skehXr7FekbUF8sLrfDbnVZAABkHcLHCLxxoEPh3phmlvs1s9yvfJdDNpZYBwBgVHjsMgKtoT7ZDENBr1N+j5PgAQDAGNDzMQKfvqxKiYSp1/aflNth04LJRVaXBABA1kqq52PVqlVqbGxUIBBQIBBQU1OTnnvuucH3P/7xj8swjCFfd911V8qLtoLNZqi6wKtw74DVpQAAkNWS6vmorq7WihUrNGPGDJmmqdWrV2vp0qXavHmz5syZI0n6yle+oh/+8IeD3+P1elNbsUVe3t2mUp9bN8wqs7oUAACyWlLh47bbbhtyfP/992vVqlV67bXXBsOH1+tVRUVF6irMAHuORzStJF+Ti/OtLgUAgKw36gGn8Xhcv/nNb9Td3a2mpqbB13/961+rpKREc+fOVXNzs3p6Lr0EeTQaVTgcHvIFAAByV9IDTrdt26ampib19fXJ5/Pp6aefVkNDgyTp9ttv1+TJk1VVVaWtW7fqu9/9rnbv3q3f/e53w17vgQce0H333Tf6nyANZpb79dKu4/R8AACQAoZpmmYy39Df36+DBw8qFArpqaee0uOPP67169cPBpBzvfTSS/rEJz6hvXv3qq6u7qLXi0ajikajg8fhcFg1NTUKhUIKBAJJ/jjjwzRNvbSrTZ+YXW51KQAAjMn6Pe26fmZpyq8bDocVDAZHdP9OuufD5XJp+vTpkqQFCxbojTfe0MMPP6xHH330gnOvuuoqSbpk+HC73XK73cmWkVYDCVNOO0uiAACQCmO+oyYSiSE9F+fasmWLJKmysnKsH2Mpp92meCKpDiIAADCMpHo+mpubtWTJEtXW1ioSiWjNmjVat26dWlpatG/fPq1Zs0a33HKLiouLtXXrVt1zzz267rrr1NjYOF71AwCALJNU+Ghra9OyZcvU2tqqYDCoxsZGtbS0aNGiRTp06JBefPFFPfTQQ+ru7lZNTY0++9nP6vvf//541Z5Wpuj5AAAgFZIKH0888cSw79XU1Gj9+vVjLigTtYZ6VR7wWF0GAAA5gVGUI+C029TbH7e6DAAAcgLhYwRKfG6Zkl7e1aZIX8zqcgAAyGrsajtCH51SpIF4Qq8f6FBvf1w3ziqTYRhWlwUAQNah5yMJDrtNC+tKtGByof747gmrywEAICsRPkahwOtSLJ5QkovDAgAAET5G7cqpRVq3p93qMgAAyDqEj1Hye5wq93u09fApq0sBACCrED7GoKEqIK/Lrue3tyrUyywYAABGgtkuYzS9zK+6Up9eefeE7IahK6cWyeUg0wEAMBzCRwoYhqHrZ5aqp39AG/ef1EDcVFNdsTxOu9WlAQCQcfgvegp5XQ5dO6NU82sK9MLO4zoW6rO6JAAAMg7hYxwU5rt0a2OlXj/QwXRcAADOQ/gYJ4Zh6LoZJdqw76TVpQAAkFEIH+NoIGHqwMluq8sAACCjED7GUYnPrUWzy/XGgQ6rSwEAIGMQPsZZWcCjo6d61dsft7oUAAAyAuEjDa6bUaqn3jpsdRkAAGQEwkcavNvWpRllPqvLAAAgIxA+0uDKqUWqLfLq+e3HdPRUr9XlAABgKVY4TZOqgjxVFeTp2W2tKvS6lOdi9VMAwMREz0ea3TynQi+8c9zqMgAAsAzhI83sNkNzqgLaf4L1PwAAExPhwwK9/XHlu3nsAgCYmAgfFjjRFVWZ32N1GQAAWILwYYFYnM3mAAATF7NdLOBx2rRh7wnFEqZM05TP7dBHphRZXRYAAGlB+LDAtTNK1RUdkNdpl81mqDXUqxd2HldRvlNX1BbKMAyrSwQAYNzw2MUiPrdDNtvpkFEZzNOihnJNLfHpd28dkWnyWAYAkLvo+cggRfkufWJ2mf73nTbZbYZqi72qK2VZdgBAbqHnI8MUeF26qaFcN8wq00Dc1PPbWxXui1ldFgAAKUPPRwarr/BrZrlPL77TJrfDpoqgR3WlPtltjAkBAGQvwkeGMwxDixrKNRBP6HgkqlfebVf/QELXziiR18UfHwAg+3D3yhIOu02TCvI0qSBPiYSp/956VLc2VtELAgDIOoz5yEI2m6Gb51Tov7YcUXd0wOpyAABICuEjS3mcdn1m/iT9YVur1aUAAJAUwkcWs9kMzarw60RX1OpSAAAYMcJHluofSCjUG1NFwKOdR8NWlwMAwIgx4DQLhXpjem5bq+or/IonTM0s91tdEgAAI0b4yDJbD5/Sya5+fe6jNewBAwDISoSPLNEVHdAre9o1tyqoxuoCq8sBAGDUCB9ZINIX04vvHNdtjVVy2BmmAwDIboSPDNY/kNAbBzrUHR3QJ+dUEjwAADmB8JFhNr53Ul3RAdlshlx2m+bXFCjfzR8TACB3cFfLEH2xuF7YeVwfnVKkiqDH6nIAABg3hA8LvXs8osOneiVJdsPQLfMq2asFAJDzCB9pdKijR/vau2Sap49LfG7dUF9mbVEAAKQZ4SMN4glTL75zXJMK8vRxwgYAYIIjfKTBK3vadf3MUnmcdqtLAQDAcknN3Vy1apUaGxsVCAQUCATU1NSk55577oLzTNPUkiVLZBiG/vM//zNVtWYvQwQPAADOSCp8VFdXa8WKFXrzzTe1adMm3XjjjVq6dKl27Ngx5LyHHnqIpb/PZVpdAAAAmSOpxy633XbbkOP7779fq1at0muvvaY5c+ZIkrZs2aKf/exn2rRpkyorK1NXKQAAyAmjHvMRj8e1du1adXd3q6mpSZLU09Oj22+/XY888ogqKipGdJ1oNKpoNDp4HA7nzvbwr+xpVyyeYN0OAADOkfR63du2bZPP55Pb7dZdd92lp59+Wg0NDZKke+65RwsXLtTSpUtHfL0HHnhAwWBw8KumpibZkjKSaZra9H6nbpxVpjlVQavLAQAgYyTd81FfX68tW7YoFArpqaee0vLly7V+/Xrt3btXL730kjZv3pzU9Zqbm/XNb35z8DgcDmd9ADl4skfbj4Z05zVTGfsCAMB5DNM0xzQc8qabblJdXZ3y8vL0z//8z7LZznamxONx2Ww2XXvttVq3bt2IrhcOhxUMBhUKhRQIBMZSWlrF4gntOR7Rkc5eVQQ9bHsPAMhI688s/5Bqydy/x7zORyKRUDQa1X333ae//Mu/HPLevHnz9OCDD14wUDXXJBKm/rC1VU11xZpdEZCNJdIBABhWUuGjublZS5YsUW1trSKRiNasWaN169appaVFFRUVFx1kWltbq6lTp6as4Ey06f1OzSz3qzzAwFIAAD5MUuGjra1Ny5YtU2trq4LBoBobG9XS0qJFixaNV31Z4cqpRdpzPKJnt7Xqk3Mq6PkAAOASkgofTzzxRFIXH+Nwkqzy7vEu9cbiig4klOdiNVMAAIaT9FRbXNySuRUqznfpWLjP6lIAAMhohI8U2XzolBKmqVBvzOpSAADIaOxqmyInuqK6ec7IVnUFAGAio+cjRYryXero7re6DAAAMh7hI0WOdPYqmOe0ugwAADIe4SNFmuqKtfXwKavLAAAg4xE+UqQ84NHxcFSJxMSZXgwAwGgQPlLo6unF2ri/w+oyAADIaISPFGoN9ak84La6DAAAMhrhI4VshnSos9fqMgAAyGiEjxSaXuaX3+PQia6o1aUAAJCxCB8p0hUd0Po97Wo91Sefm7XbAAAYDnfJFEgkTLVsP6ZPz6+S006eAwDgUrhTpsju4xG9tKvN6jIAAMh4hI8UiA4kNG9SkL1dAAAYAcJHCuS57OqNxa0uAwCArED4SAHTNNU/kLC6DAAAsgLhIwVe2tWm/3f5JKvLAAAgKxA+UmAgYcrrsltdBgAAWYHwkQJXTy/R05uPWF0GAABZgXU+xujtQ6d0PNynBZMLrS4FAICsQPgYg7cPnVIwz6nLagqsLgUAgKxB+BiFI6d6tedYRDKkxuqg1eUAAJBVCB9JOtTRo8OdvbphVpnVpQAAkJUYcJqEw509erctoqa6YqtLAQAgaxE+RijUG9Ou1ohunFVudSkAAGQ1wscI7TgSoscDAIAUYMzHRbRF+vRee7f8Hofqy/3aeiQkScp301wAAIwVd9MzogNx/WFrqwrzXQp4nJpfU6Bwb0wb9p1UfYVf5QGP1SUCAJATCB9nuB12Fea7VFfiU22xV5LkcdpVRugAACClGPNxjhvqy7SzNSzTNK0uBQCAnEX4OM/8mgLtOBq2ugwAAHIW4eM8x8J9jO8AAGAcET7Osf1ISJG+mEr9bqtLAQAgZxE+zvjTuyfkddl17YxSq0sBACCnET7OmFzs1Z7jEb11sFN9sbjV5QAAkLOYantGTZFXNUVenerp11vvd6qjp183z6mQ004+AwAglbiznqfA69IVkwvlsBk60tlrdTkAAOQcwsdFhHpjcjvsmlKSb3UpAADkHMLHRZQHPPK67DrRFbW6FAAAcg7hYxjd/QMq8THlFgCAVCN8DMNus2kgnrC6DAAAcg7hYxgD8YQczHQBACDluLtehGmaiifYXA4AgPFA+LiIlh3HdNW0YqvLAAAgJxE+zrPtcEjzqgsUzHNaXQoAADmJ8HGetkifJhXkWV0GAAA5i/BxHrvN0PPbj1ldBgAAOSup8LFq1So1NjYqEAgoEAioqalJzz333OD7X/va11RXV6e8vDyVlpZq6dKl2rVrV8qLHk8fry/TvOqgNuw7YXUpAADkpKTCR3V1tVasWKE333xTmzZt0o033qilS5dqx44dkqQFCxbol7/8pd555x21tLTINE0tXrxY8Xh27RI7qSBPiYQU6YupKzqgtw+dsrokAAByhmGa5pjmlBYVFWnlypW68847L3hv69atuuyyy7R3717V1dWN6HrhcFjBYFChUEiBQGAspY1JXyyu/32nTXabNKcqqB1HQ/rIlCIV5DkV7hvQ5oOdg+uAmKYpwzA0v7pAQS8DVQEAmWv9nnZdP7M05ddN5v7tGO2HxONxrV27Vt3d3Wpqarrg/e7ubv3yl7/U1KlTVVNTM9qPsYzHadenGisHj0v9br17vEu7j0Xkcdp146wyGYYx+H4iYer/9p1QZdCj6WV+K0oGACArJB0+tm3bpqamJvX19cnn8+npp59WQ0PD4Ps///nP9Z3vfEfd3d2qr6/XCy+8IJfLNez1otGootGzG7iFw+FkS0oLj9OuedXBYd+32QxdO6NUL+w8TvgAAOASkp7tUl9fry1btmjjxo26++67tXz5cu3cuXPw/S984QvavHmz1q9fr5kzZ+rP//zP1dfXN+z1HnjgAQWDwcGvbOwlOVe+y66+WHaNcQEAIJ3GPObjpptuUl1dnR599NEL3uvv71dhYaEef/xxff7zn7/o91+s56OmpsbyMR+j1ReL6+VdbbLbDFUXetVQlX0/AwAgd2X1mI8PJBKJIeHhXKZpyjTNYd+XJLfbLbc7d7au9zjtWjKvUpsPdsrnHnPzAgCQc5K6OzY3N2vJkiWqra1VJBLRmjVrtG7dOrW0tOi9997Tb3/7Wy1evFilpaU6fPiwVqxYoby8PN1yyy3jVX9GSiRMHQ/36fLaQqtLAQAg4yQVPtra2rRs2TK1trYqGAyqsbFRLS0tWrRokY4ePao//vGPeuihh9TZ2any8nJdd9112rBhg8rKysar/oxksxkq8Lr0TmtYsyt57AIAwLnGPOYj1TJlnY9UeHl3m26on1jBCwCQ2TJhzAd7u4wj48NPAQBgwiF8jJMM61ACACBjED7GyfFwVAXe4RdXAwBgomIuaIqZpqmN+zs0EDd19fRiq8sBACDjED5S6NV9J9UdHdDltQUq9uXO2iUAAKQS4SNF/mfHMTVWF6gi6LG6FAAAMhpjPlKgtz8uv8dJ8AAAYAQIHylgGFKot9/qMgAAyAo8djnPya6oTnT1q77Cf8nzdh+LaF97l7wuuyJ9A5pfU5CeAgEAyHKEj3O8ezyitkhUNYVerdvdJkkyDEOmacowDPX2x+Vxnu4sKvN7dO2MEvk9TitLBgAg6xA+zjjZFdXhzl7dMOv0cui1xV6LKwIAIDcx5kOnd6H9v30nB4MHAAAYP4QPSev2tOmm2QQPAADSYcKHjyOneuVx2OV18QQKAIB0mNB33B1HQ+rsjumaGSVWlwIAwIQx4cJHT/+AWkN92nMsopoiL8EDAIA0m3CPXV7a1SaHzdDCuhLNnRS0uhwAACacCRc+agq96oslFPSyPgcAAFaYcOEj3BdTvttudRkAAExYEyp8xOIJbXyvQy07jltdCgAAE9aEGnDqtNu0qKFcnT1sAgcAgFUmVM+HJL3TGtbCOma4AABglQkVPkI9Me0+HtFAImF1KQAATFgTKny4nTY1VAb05vudao9ErS4HAIAJaUKN+fA47fqzj9RoIJ7QrmMRbT8aGnyvptCrutJ8GYZhYYUAAOS+CRU+PuCw24YsMGaapg539mr9nnaZZ14L5jk1pyogt4NpuQAApNKEDB/nMwxDNUVe1RR5B1871dOvTQc61R8/PT7EZbdpblWQxckAABgjwscwCrwuXT397KyY6EBcO46GFeqNSZIMSXWlPlUX5vGoBgCAJBA+RsjtsOuK2sLBY9M0ta+9W+v2tJ95QSrxuTW70i+HfUKN4wUAICmEj1EyDEPTy3yaXuYbfK09EtWGfScVN0+PHPE47JpXHZTPTTMDAPAB7oopVOp3q9RfOnjc0z+g7UfC6o4OSJJsNkP15X5VBD1WlQgAgOUIH+PI63LoyqlFg8fxhKndxyJ6pzU8+FpVQZ5mlPlkszFuBAAwMRA+0shuM9RQFVBDVWDwtSOnevXKu+0686RGPo9D8yYF5XEyxRcAkJsIHxabVJCnSQV5g8fhvpjeOtipaOz0FF+H3VBDZUDFPrdVJQIAkFKEjwwT8DiHbHwXiye082hYWw+fXY11crFXU0tYjRUAkJ0IHxnOabfpspqCwWPTNHXgZI/W7W4ffK0o36WGqoCcTPEFAGQBwkeWMQxDU0vyNbUkf/C1E11RvbrvpOKJM1N8nUzxBQBkLu5OOaDE59Z1M4dO8d12OKSe/rik01N8Z1f4VRZgii8AwHqEjxzkdTl01bTiweMPdvHd8cEUX1OqKWIXXwCANQgfE8DFdvE91NF7emn4M1N8C7xOzakKyuVg3AgAYHwRPiYgwzBUW+xVbfHZXXw7uvu1cf9JDcRNyZDcDpvmTQrK72EXXwBAahE+IOn0jJlrZ5wdN9IXi2v7kZAiZ5aGNyTNqgiwNDwAYMwIH7goj9Ouj0wZujT8nuMRvXPs7LiRSYV5ml7K0vAAgOQQPjAidpuh2ZUBza48uzT84c6e00vDnzkOeJyaOykgt4Ol4QEAwyN8YNSqC72qLjw7biTUE9OmA53qj59eGt51ZqBrMI9xIwCAswgfSJmg16mrp59dGr4vFteOoyGF+xg3AgA4i/CBceNx2rVg8tBxI7uPDR03Ulvs1TT2qQGACYXwgbSx2ww1VAXUoNPjRkzT1MGOniHrjZT43Jpd6ZeDfWoAIGcRPmAZwzA0uThfk4vP7lPTFunT/+07qcSZfWry3Q41VgflcTKIFQByRVL/vVy1apUaGxsVCAQUCATU1NSk5557TpLU0dGhv/qrv1J9fb3y8vJUW1urb3zjGwqFQh9yVeCsMr9H188s1Q2zynTDrDLNrvRr88FTenl3m17e3ab/23tCoZ6Y1WUCAMYgqZ6P6upqrVixQjNmzJBpmlq9erWWLl2qzZs3yzRNHT16VD/96U/V0NCg999/X3fddZeOHj2qp556arzqR47ze5xqqju7T010IK7tR8IK98ZkypRhGJpZ7tekgjwLqwQAJMMwTdP88NOGV1RUpJUrV+rOO++84L21a9fqi1/8orq7u+VwjCznhMNhBYNBhUIhBQKBD/8GTGiJhKk9bREdPdUrQ6cHrVYEPZpZ7pedxc8A4ALr97Tr+nN2Qk+VZO7fox7zEY/HtXbtWnV3d6upqemi53xQwKWCRzQaVTQaHTwOh8OjLQkTkM1maFZFQLMqzv5FP3qqV398t10fxGqfx6F5kxg3AgCZIunwsW3bNjU1Namvr08+n09PP/20GhoaLjjvxIkT+tGPfqSvfvWrl7zeAw88oPvuuy/ZMoBhVRXkqeqcxzDhvpjeOtipaOz04mcOu6E5VUEV5busKhEAJrSkH7v09/fr4MGDCoVCeuqpp/T4449r/fr1QwJIOBzWokWLVFRUpGeeeUZO5/ArXF6s56OmpobHLhg3/QMJ7WwNq7O7f/C1qSX5mlzsZb0RADkvEx67jHnMx0033aS6ujo9+uijkqRIJKKbb75ZXq9Xv//97+XxJLeaJWM+kG6maWr/iW69f7Jn8LVin0sNlQHWGwGQczIhfIx5nY9EIjHYcxEOh3XzzTfL7XbrmWeeSTp4AFYwDEPTSn2aVuobfK09EtWGfScVT5iSIXkcds2rDsrnZmkcABirpH6TNjc3a8mSJaqtrVUkEtGaNWu0bt06tbS0KBwOa/Hixerp6dG//du/KRwODw4eLS0tld3OYD9kj1K/W6X+s/8z6Okf0LbDIfX0xyVJhiHNrgyoPEDABoBkJRU+2tratGzZMrW2tioYDKqxsVEtLS1atGiR1q1bp40bN0qSpk+fPuT79u/frylTpqSsaCDdvC6Hrpp2dr2RgXhCu45FtPPo6YBtylRNoVd1pT7ZmOILAJc05jEfqcaYD2Qj0zR1uLNXe9u7zrwgBfIcmjspKLeDXj8AmSMnxnwAOD1upKbIq5oi7+BroZ6YNh3oVH88IZmS027T3EkBFXiZ4gtgYiN8AOMk6HXq6uklg8fRgbh2HA0r1HPq9AuGNL3Up+rCPKb4AphQCB9Amrgddl1RWzh4bJqm9rV3a93u9sHXSv1uzarwM8UXQE4jfAAWMQxD08t8ml52dopvW6Tv7BRfSXkuu+ZNCiqfKb4Acgi/0YAMUub3qMx/dvpud3RA24+E1BM7PcXXZhiaXeFXGVN8AWQxwgeQwfLdF5/iu6P1zAaMplRTlKdpJUzxBTAymTDJlfABZBGH3aa5k4KDxx9M8X3l3XZ98Osk4HFq7qQAU3wBXFQmDHAnfABZ7EOn+Epy2W2aWxVU0Dv8Bo8AJg56PgCk3LBTfHtjkiRDUh1TfAFYiPAB5Lhhp/juOWeKr48pvsBEkQn/6SB8ABPMJaf4mqYMnR7oOm9SUB4n40YApB7hA8AFU3wjfTG9dbBT0YFzxo1MCiqYx7gRINsx5gNARvJ7nFpYN3TcyPYjIYX7BiSdXm9kVoVf5aw3AmAUCB8APpTbYdeCyUWDx/GEqV3Hwtp5znojU0ryNaXYmxHPkwEMLxP+jRI+ACTNbjM0pyqoOWeOTdPU+yd7tP7MIFZTpwexzq4MyM7iZwDOQ/gAMGaGYZzu+SjJH3ytLdynP+09ocSZ58sBj0NzqhjECliNMR8AclZZwDNkD5pQb0xvvd+p6JnFz9x2m+ZWBxXwMIgVmGgIHwDSIpjn1MJzFj/ri50exBqJnh3EOrvSP2TWDYDUY8wHgAnL47TrI1PODmId3DTvaHjwtWkl+aotYhArkGsIHwAywsU2zXvvxNCVWMv9HtVX+BnECowBYz4AYBiGYaiu1Ke60rMrsR4LnT+IlR18gWxE+ACQNSqCHlUEzxnEeu4OvqbkdtrUWF0gn5tfbcBwMuExJv9CAWSt83fw7e2Pa9uRkLr7Tw9iddgMza0KqjDfZVWJAC6C8AEgZ+S57Lpy6tlBrP0DCe04GtKWw6cknZlRU+EfMgUYmGgY8wEA48jlsOny2sLB43jC1DutYe1oDZ9ehtWQZpT5VF3ota5IYAIifACYMOw244IZNXvbuvTy7rYzL0i1xV5NK8nPiOfiwHjIhL/bhA8AE5ZhGJpR7teMcr+k02HkYEfPkOm9FQGP6sv9sjG9F0gZwgcAnGEYhiYX52ty8dk9alpDvXrl3XaZkmRKRfkuzakKyGG3WVYnMBaM+QCADFcZzFNlMG/w+ERXVBv2nVT8zC9wv9uhuZPYMA9IBuEDAJJQ4nPrupmlg8fnb5iX57SrsToor4tfr8hMjPkAgCx3/oZ5Pf0D2nY4pJ5YXJLkOrNsfDCP3XuBDxA+ACCFvC6HrppWPHgcHTi9e2+47/TCZ3bDUENVQCU+t1UlYoJjzAcA5Di3w64Fk4fu3ruzNaxth0MyjMzoAsfEkgnBl/ABAGnksJ/efwaYyJgrBgAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0orwAQAA0sphdQHnM01TkhQOhy2uBAAAjNQH9+0P7uOXknHhIxKJSJJqamosrgQAACQrEokoGAxe8hzDHElESaNEIqGjR4/K7/fLMIyLnhMOh1VTU6NDhw4pEAikucLsRbuNDu02OrTb6NBuo0O7jU4q2800TUUiEVVVVclmu/Sojozr+bDZbKqurh7RuYFAgL9ko0C7jQ7tNjq02+jQbqNDu41Oqtrtw3o8PsCAUwAAkFaEDwAAkFZZGT7cbrfuvfdeud1uq0vJKrTb6NBuo0O7jQ7tNjq02+hY1W4ZN+AUAADktqzs+QAAANmL8AEAANKK8AEAANKK8AEAANIqo8PH/fffr4ULF8rr9aqgoOCi53zjG9/QggUL5Ha7NX/+/AveP3DggAzDuODrtddeG9/iLZSKdjvX3r175ff7h71WrkhFu+3evVs33HCDysvL5fF4NG3aNH3/+99XLBYb3+ItlIp2W7dunZYuXarKykrl5+dr/vz5+vWvfz2+hVssFe3W19enL33pS5o3b54cDoc+85nPjGvNmSJVv+O2bt2qa6+9Vh6PRzU1NfrJT34yfkVngJG028GDB/WpT31KXq9XZWVl+va3v62BgYEh5zzyyCOaPXu28vLyVF9fr3/9139NupaMDh/9/f36sz/7M919992XPO/LX/6yPve5z13ynBdffFGtra2DXwsWLEhlqRklle0Wi8X0+c9/Xtdee20qS8xIqWg3p9OpZcuW6X/+53+0e/duPfTQQ/rFL36he++9dzxKzgipaLcNGzaosbFR//Ef/6GtW7fqjjvu0LJly/T73/9+PErOCKlot3g8rry8PH3jG9/QTTfdNB5lZqRUtF04HNbixYs1efJkvfnmm1q5cqV+8IMf6LHHHhuPkjPCh7VbPB7Xpz71KfX392vDhg1avXq1fvWrX+kf/uEfBs9ZtWqVmpub9YMf/EA7duzQfffdp69//ev67//+7+SKMbPAL3/5SzMYDF7ynHvvvde87LLLLnh9//79piRz8+bN41JbJhtLu33gO9/5jvnFL35xRNfKFalot3Pdc8895jXXXDP2wjJcqtvtlltuMe+4446xF5bhUtVuy5cvN5cuXZqyurLBWNru5z//uVlYWGhGo9HB17773e+a9fX1Ka4y8wzXbs8++6xps9nMY8eODb62atUqMxAIDLZTU1OT+bd/+7dDvu+b3/ymefXVVydVQ0b3fKTSpz/9aZWVlemaa67RM888Y3U5WeGll17S2rVr9cgjj1hdStbau3evnn/+eV1//fVWl5J1QqGQioqKrC4DOerVV1/VddddJ5fLNfjazTffrN27d6uzs9PCyqzz6quvat68eSovLx987eabb1Y4HNaOHTskSdFoVB6PZ8j35eXl6fXXX0/q8XLOhw+fz6ef/exnWrt2rf7whz/ommuu0Wc+8xkCyIc4efKkvvSlL+lXv/oVmzSNwsKFC+XxeDRjxgxde+21+uEPf2h1SVnl3//93/XGG2/ojjvusLoU5Khjx44NuclKGjw+duyYFSVZbiRtcvPNN+vxxx/Xm2++KdM0tWnTJj3++OOKxWI6ceLEiD8r7eHje9/73kUHgJ77tWvXrpR9XklJib75zW/qqquu0kc/+lGtWLFCX/ziF7Vy5cqUfUY6pLvdvvKVr+j222/Xddddl7JrWiHd7faB3/72t3rrrbe0Zs0a/eEPf9BPf/rTlH/GeLKq3STp5Zdf1h133KFf/OIXmjNnzrh8xnixst2yHW03Oulut7//+7/XkiVL9LGPfUxOp1NLly7V8uXLJZ3elX6kHCmraIS+9a1v6Utf+tIlz5k2bdq41nDVVVfphRdeGNfPSLV0t9tLL72kZ555ZvCmaZqmEomEHA6HHnvsMX35y19O2WeNJ6v+vtXU1EiSGhoaFI/H9dWvflXf+ta3ZLfbU/5Z48Gqdlu/fr1uu+02Pfjgg1q2bFnKrz/eMuH3W7ZKd9tVVFTo+PHjQ1774LiioiJlnzPeUtluFRUVev3114e8dn6b5OXl6cknn9Sjjz6q48ePq7KyUo899pj8fr9KS0tHXHfaw0dpaWlSBY6HLVu2qLKy0tIakpXudnv11VcVj8cHj//rv/5LP/7xj7VhwwZNmjQpbXWMVSb8fUskEorFYkokElkTPqxot3Xr1unWW2/Vj3/8Y331q19N62enSib8fctW6W67pqYm/d3f/Z1isZicTqck6YUXXlB9fb0KCwvTVsdYpbLdmpqadP/996utrU1lZWWSTrdJIBBQQ0PDkHOdTqeqq6slSb/5zW906623ZnbPRzIOHjyojo4OHTx4UPF4XFu2bJEkTZ8+XT6fT9LpAX1dXV06duyYent7B89paGiQy+XS6tWr5XK5dPnll0uSfve73+nJJ5/U448/bsWPlBapaLfZs2cPueamTZtks9k0d+7cdP4oaZWKdvv1r38tp9OpefPmye12a9OmTWpubtbnPve5wV9wuSYV7fbyyy/r1ltv1V//9V/rs5/97ODzZZfLlbODTlPRbpK0c+dO9ff3q6OjQ5FIZPCcD1u/J5ulou1uv/123Xfffbrzzjv13e9+V9u3b9fDDz+sBx980KKfavx9WLstXrxYDQ0N+ou/+Av95Cc/0bFjx/T9739fX//61wd3vd2zZ49ef/11XXXVVers7NQ//dM/afv27Vq9enVyxSQ7RSedli9fbkq64Ovll18ePOf666+/6Dn79+83TdM0f/WrX5mzZ882vV6vGQgEzCuvvNJcu3atNT9QmqSi3c43EabapqLdfvOb35hXXHGF6fP5zPz8fLOhocH8x3/8R7O3t9eaHyoNUtFuw13j+uuvt+RnSodU/TudPHnyRc/JZalqu7ffftu85pprTLfbbU6aNMlcsWJF+n+YNBpJux04cMBcsmSJmZeXZ5aUlJjf+ta3zFgsNvj+zp07zfnz55t5eXlmIBAwly5dau7atSvpWgzTNM3k4goAAMDo5fxUWwAAkFkIHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK0IHwAAIK3+f7bybQ4VF8xHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above: underused < 0.5; below overused > 1.6\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Did I grossly overuse any units, or skip any live ones?\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.5 and unitPop[u] > 5:\n",
    "        plotPoly(unitGeom[u])\n",
    "        #print(u,unitPop[u], unitUse[u])\n",
    "        #for c in range(nCounties):\n",
    "        #    if u in countyUnitList[c]:\n",
    "        #        print(u,\"is in county\",c)\n",
    "        #        print(\"its neighbor units are\",unitNbrs[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"above: underused < 0.5; below overused > 1.6\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 1.6:\n",
    "        plotPoly(unitGeom[u],0.3)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 1538 populated HDs out of 1538\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs out of\",nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGfCAYAAABBU+jJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAqGElEQVR4nO3dfXRU5YHH8d+EkASRmRBoZpg2AbbrIrEULWgYFVtLDgGiKzWuBbOY2hzSpQmugEhyqogvNRhZBVwki2sbzhYP6u7CKqzRFJBUHSMGs2CE+FIwUZhEN2aGYAl5uftHD3cdCEpw8vKk388595zOvc+d+9xbJN8zuXNxWJZlCQAAwCBRfT0BAACA7iJgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHGiu7tDRUWFHnnkEVVVVeno0aPasmWLZs+eHTbmwIEDWrZsmXbv3q329nalpKToP/7jP5ScnCxJOnHihJYsWaLNmzertbVV6enpeuKJJ+R2u+33qKur04IFC7Rr1y5deOGFys7OVlFRkaKjz23KnZ2dOnLkiIYNGyaHw9Hd0wQAAH3AsiwdO3ZMXq9XUVFn/5yl2wFz/PhxTZw4UT//+c914403nrH9ww8/1NVXX62cnBzdd999cjqdqqmpUVxcnD1m0aJF2r59u5577jm5XC7l5+frxhtv1GuvvSZJ6ujoUEZGhjwej15//XUdPXpUt956qwYPHqyHHnronOZ55MgRJSUldff0AABAP1BfX6/vfOc7Z93u+Cb/mKPD4TjjE5g5c+Zo8ODB+rd/+7cu9wkGg/rWt76lp59+WjfddJMk6eDBgxo/frz8fr+mTJmiF198Udddd52OHDlifypTUlKiZcuW6dNPP1VMTMzXzi0YDCo+Pl719fVyOp3ne4oAAKAXhUIhJSUlqbm5WS6X66zjuv0JzFfp7OzU9u3bdddddyk9PV1vv/22xo4dq8LCQjtyqqqq1NbWprS0NHu/iy++WMnJyXbA+P1+TZgwIexXSunp6VqwYIFqamp02WWXnXHs1tZWtba22q+PHTsmSXI6nQQMAACG+brbPyJ6E29jY6NaWlq0cuVKzZgxQy+//LJ+8pOf6MYbb9Tu3bslSYFAQDExMYqPjw/b1+12KxAI2GO+HC+ntp/a1pWioiK5XC574ddHAAAMXBENmM7OTknSDTfcoEWLFunSSy9VQUGBrrvuOpWUlETyUGcoLCxUMBi0l/r6+h49HgAA6DsRDZiRI0cqOjpaKSkpYevHjx+vuro6SZLH49HJkyfV3NwcNqahoUEej8ce09DQcMb2U9u6Ehsba/+6iF8bAQAwsEU0YGJiYnT55ZertrY2bP17772n0aNHS5ImTZqkwYMHa8eOHfb22tpa1dXVyefzSZJ8Pp/279+vxsZGe0x5ebmcTucZcQQAAP7ydPsm3paWFn3wwQf260OHDqm6uloJCQlKTk7W0qVL9dOf/lTXXHONrr32WpWVlemFF17QK6+8IklyuVzKycnR4sWLlZCQIKfTqYULF8rn82nKlCmSpOnTpyslJUXz5s1TcXGxAoGA7r77buXl5Sk2NjYyZw4AAMxlddOuXbssSWcs2dnZ9pinnnrK+uu//msrLi7OmjhxorV169aw9/jTn/5k/fKXv7SGDx9uXXDBBdZPfvIT6+jRo2FjDh8+bM2cOdMaMmSINXLkSGvJkiVWW1vbOc8zGAxakqxgMNjdUwQAAH3kXH9+f6PnwPRnoVBILpdLwWCQ+2EAADDEuf785t9CAgAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGCcbj+JFwD6gzEF2/t6Ct12eGVGX08BGDD4BAYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHG6HTAVFRW6/vrr5fV65XA4tHXr1rOO/Yd/+Ac5HA6tXr06bH1TU5OysrLkdDoVHx+vnJwctbS0hI3Zt2+fpk6dqri4OCUlJam4uLi7UwUAAANUtwPm+PHjmjhxotatW/eV47Zs2aI33nhDXq/3jG1ZWVmqqalReXm5tm3bpoqKCuXm5trbQ6GQpk+frtGjR6uqqkqPPPKIVqxYoQ0bNnR3ugAAYACK7u4OM2fO1MyZM79yzCeffKKFCxfqpZdeUkZGRti2AwcOqKysTHv27NHkyZMlSY8//rhmzZqlVatWyev1atOmTTp58qR+85vfKCYmRpdccomqq6v16KOPhoUOAAD4yxTxe2A6Ozs1b948LV26VJdccskZ2/1+v+Lj4+14kaS0tDRFRUWpsrLSHnPNNdcoJibGHpOenq7a2lp9/vnnXR63tbVVoVAobAEAAANTxAPm4YcfVnR0tG6//fYutwcCASUmJoati46OVkJCggKBgD3G7XaHjTn1+tSY0xUVFcnlctlLUlLSNz0VAADQT0U0YKqqqrRmzRqVlpbK4XBE8q2/VmFhoYLBoL3U19f36vEBAEDviWjA/OEPf1BjY6OSk5MVHR2t6OhoffTRR1qyZInGjBkjSfJ4PGpsbAzbr729XU1NTfJ4PPaYhoaGsDGnXp8ac7rY2Fg5nc6wBQAADEwRDZh58+Zp3759qq6uthev16ulS5fqpZdekiT5fD41NzerqqrK3m/nzp3q7OxUamqqPaaiokJtbW32mPLyco0bN07Dhw+P5JQBAICBuv0tpJaWFn3wwQf260OHDqm6uloJCQlKTk7WiBEjwsYPHjxYHo9H48aNkySNHz9eM2bM0Pz581VSUqK2tjbl5+drzpw59leub7nlFt13333KycnRsmXL9M4772jNmjV67LHHvsm5AgCAAaLbAfPWW2/p2muvtV8vXrxYkpSdna3S0tJzeo9NmzYpPz9f06ZNU1RUlDIzM7V27Vp7u8vl0ssvv6y8vDxNmjRJI0eO1PLly/kKNQAAkCQ5LMuy+noSPSEUCsnlcikYDHI/DDAAjSnY3tdT6LbDKzO+fhDwF+5cf37zbyEBAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwTrcDpqKiQtdff728Xq8cDoe2bt1qb2tra9OyZcs0YcIEDR06VF6vV7feequOHDkS9h5NTU3KysqS0+lUfHy8cnJy1NLSEjZm3759mjp1quLi4pSUlKTi4uLzO0MAADDgdDtgjh8/rokTJ2rdunVnbPviiy+0d+9e3XPPPdq7d6/+8z//U7W1tfrbv/3bsHFZWVmqqalReXm5tm3bpoqKCuXm5trbQ6GQpk+frtGjR6uqqkqPPPKIVqxYoQ0bNpzHKQIAgIHGYVmWdd47OxzasmWLZs+efdYxe/bs0RVXXKGPPvpIycnJOnDggFJSUrRnzx5NnjxZklRWVqZZs2bp448/ltfr1fr16/WrX/1KgUBAMTExkqSCggJt3bpVBw8ePKe5hUIhuVwuBYNBOZ3O8z1FAP3UmILtfT2Fbju8MqOvpwD0e+f687vH74EJBoNyOByKj4+XJPn9fsXHx9vxIklpaWmKiopSZWWlPeaaa66x40WS0tPTVVtbq88//7zL47S2tioUCoUtAABgYOrRgDlx4oSWLVumuXPn2hUVCASUmJgYNi46OloJCQkKBAL2GLfbHTbm1OtTY05XVFQkl8tlL0lJSZE+HQAA0E/0WMC0tbXp5ptvlmVZWr9+fU8dxlZYWKhgMGgv9fX1PX5MAADQN6J74k1PxctHH32knTt3hv0Oy+PxqLGxMWx8e3u7mpqa5PF47DENDQ1hY069PjXmdLGxsYqNjY3kaQAAgH4q4p/AnIqX999/X7///e81YsSIsO0+n0/Nzc2qqqqy1+3cuVOdnZ1KTU21x1RUVKitrc0eU15ernHjxmn48OGRnjIAADBMtwOmpaVF1dXVqq6uliQdOnRI1dXVqqurU1tbm2666Sa99dZb2rRpkzo6OhQIBBQIBHTy5ElJ0vjx4zVjxgzNnz9fb775pl577TXl5+drzpw58nq9kqRbbrlFMTExysnJUU1NjZ555hmtWbNGixcvjtyZAwAAY3X7a9SvvPKKrr322jPWZ2dna8WKFRo7dmyX++3atUs/+tGPJP35QXb5+fl64YUXFBUVpczMTK1du1YXXnihPX7fvn3Ky8vTnj17NHLkSC1cuFDLli0753nyNWpgYONr1MDAdK4/v7/Rc2D6MwIGGNgIGGBg6jfPgQEAAIg0AgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYp9sBU1FRoeuvv15er1cOh0Nbt24N225ZlpYvX65Ro0ZpyJAhSktL0/vvvx82pqmpSVlZWXI6nYqPj1dOTo5aWlrCxuzbt09Tp05VXFyckpKSVFxc3P2zAwAAA1K3A+b48eOaOHGi1q1b1+X24uJirV27ViUlJaqsrNTQoUOVnp6uEydO2GOysrJUU1Oj8vJybdu2TRUVFcrNzbW3h0IhTZ8+XaNHj1ZVVZUeeeQRrVixQhs2bDiPUwQAAAONw7Is67x3dji0ZcsWzZ49W9KfP33xer1asmSJ7rzzTklSMBiU2+1WaWmp5syZowMHDiglJUV79uzR5MmTJUllZWWaNWuWPv74Y3m9Xq1fv16/+tWvFAgEFBMTI0kqKCjQ1q1bdfDgwXOaWygUksvlUjAYlNPpPN9TBNBPjSnY3tdT6LbDKzP6egpAv3euP78jeg/MoUOHFAgElJaWZq9zuVxKTU2V3++XJPn9fsXHx9vxIklpaWmKiopSZWWlPeaaa66x40WS0tPTVVtbq88//7zLY7e2tioUCoUtAABgYIpowAQCAUmS2+0OW+92u+1tgUBAiYmJYdujo6OVkJAQNqar9/jyMU5XVFQkl8tlL0lJSd/8hAAAQL80YL6FVFhYqGAwaC/19fV9PSUAANBDIhowHo9HktTQ0BC2vqGhwd7m8XjU2NgYtr29vV1NTU1hY7p6jy8f43SxsbFyOp1hCwAAGJgiGjBjx46Vx+PRjh077HWhUEiVlZXy+XySJJ/Pp+bmZlVVVdljdu7cqc7OTqWmptpjKioq1NbWZo8pLy/XuHHjNHz48EhOGQAAGKjbAdPS0qLq6mpVV1dL+vONu9XV1aqrq5PD4dAdd9yhBx98UM8//7z279+vW2+9VV6v1/6m0vjx4zVjxgzNnz9fb775pl577TXl5+drzpw58nq9kqRbbrlFMTExysnJUU1NjZ555hmtWbNGixcvjtiJAwAAc0V3d4e33npL1157rf36VFRkZ2ertLRUd911l44fP67c3Fw1Nzfr6quvVllZmeLi4ux9Nm3apPz8fE2bNk1RUVHKzMzU2rVr7e0ul0svv/yy8vLyNGnSJI0cOVLLly8Pe1YMAAD4y/WNngPTn/EcGGBg4zkwwMDUJ8+BAQAA6A0EDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIwT8YDp6OjQPffco7Fjx2rIkCH67ne/qwceeECWZdljLMvS8uXLNWrUKA0ZMkRpaWl6//33w96nqalJWVlZcjqdio+PV05OjlpaWiI9XQAAYKCIB8zDDz+s9evX65//+Z914MABPfzwwyouLtbjjz9ujykuLtbatWtVUlKiyspKDR06VOnp6Tpx4oQ9JisrSzU1NSovL9e2bdtUUVGh3NzcSE8XAAAYyGF9+aORCLjuuuvkdrv11FNP2esyMzM1ZMgQ/e53v5NlWfJ6vVqyZInuvPNOSVIwGJTb7VZpaanmzJmjAwcOKCUlRXv27NHkyZMlSWVlZZo1a5Y+/vhjeb3er51HKBSSy+VSMBiU0+mM5CkC6AfGFGzv6yl02+GVGX09BaDfO9ef3xH/BObKK6/Ujh079N5770mS/ud//kevvvqqZs6cKUk6dOiQAoGA0tLS7H1cLpdSU1Pl9/slSX6/X/Hx8Xa8SFJaWpqioqJUWVnZ5XFbW1sVCoXCFgAAMDBFR/oNCwoKFAqFdPHFF2vQoEHq6OjQr3/9a2VlZUmSAoGAJMntdoft53a77W2BQECJiYnhE42OVkJCgj3mdEVFRbrvvvsifToAAKAfivgnMM8++6w2bdqkp59+Wnv37tXGjRu1atUqbdy4MdKHClNYWKhgMGgv9fX1PXo8AADQdyL+CczSpUtVUFCgOXPmSJImTJigjz76SEVFRcrOzpbH45EkNTQ0aNSoUfZ+DQ0NuvTSSyVJHo9HjY2NYe/b3t6upqYme//TxcbGKjY2NtKnAwAA+qGIfwLzxRdfKCoq/G0HDRqkzs5OSdLYsWPl8Xi0Y8cOe3soFFJlZaV8Pp8kyefzqbm5WVVVVfaYnTt3qrOzU6mpqZGeMgAAMEzEP4G5/vrr9etf/1rJycm65JJL9Pbbb+vRRx/Vz3/+c0mSw+HQHXfcoQcffFAXXXSRxo4dq3vuuUder1ezZ8+WJI0fP14zZszQ/PnzVVJSora2NuXn52vOnDnn9A0kAAAwsEU8YB5//HHdc889+uUvf6nGxkZ5vV794he/0PLly+0xd911l44fP67c3Fw1Nzfr6quvVllZmeLi4uwxmzZtUn5+vqZNm6aoqChlZmZq7dq1kZ4uAAAwUMSfA9Nf8BwYYGDjOTDAwNRnz4EBAADoaQQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjNMjAfPJJ5/o7//+7zVixAgNGTJEEyZM0FtvvWVvtyxLy5cv16hRozRkyBClpaXp/fffD3uPpqYmZWVlyel0Kj4+Xjk5OWppaemJ6QIAAMNEPGA+//xzXXXVVRo8eLBefPFFvfvuu/qnf/onDR8+3B5TXFystWvXqqSkRJWVlRo6dKjS09N14sQJe0xWVpZqampUXl6ubdu2qaKiQrm5uZGeLgAAMJDDsiwrkm9YUFCg1157TX/4wx+63G5Zlrxer5YsWaI777xTkhQMBuV2u1VaWqo5c+bowIEDSklJ0Z49ezR58mRJUllZmWbNmqWPP/5YXq/3a+cRCoXkcrkUDAbldDojd4IA+oUxBdv7egrddnhlRl9PAej3zvXnd8Q/gXn++ec1efJk/d3f/Z0SExN12WWX6cknn7S3Hzp0SIFAQGlpafY6l8ul1NRU+f1+SZLf71d8fLwdL5KUlpamqKgoVVZWdnnc1tZWhUKhsAUAAAxMEQ+YP/7xj1q/fr0uuugivfTSS1qwYIFuv/12bdy4UZIUCAQkSW63O2w/t9ttbwsEAkpMTAzbHh0drYSEBHvM6YqKiuRyuewlKSkp0qcGAAD6iYgHTGdnp37wgx/ooYce0mWXXabc3FzNnz9fJSUlkT5UmMLCQgWDQXupr6/v0eMBAIC+E/GAGTVqlFJSUsLWjR8/XnV1dZIkj8cjSWpoaAgb09DQYG/zeDxqbGwM297e3q6mpiZ7zOliY2PldDrDFgAAMDBFPGCuuuoq1dbWhq177733NHr0aEnS2LFj5fF4tGPHDnt7KBRSZWWlfD6fJMnn86m5uVlVVVX2mJ07d6qzs1OpqamRnjIAADBMdKTfcNGiRbryyiv10EMP6eabb9abb76pDRs2aMOGDZIkh8OhO+64Qw8++KAuuugijR07Vvfcc4+8Xq9mz54t6c+f2MyYMcP+1VNbW5vy8/M1Z86cc/oGEgAAGNgiHjCXX365tmzZosLCQt1///0aO3asVq9eraysLHvMXXfdpePHjys3N1fNzc26+uqrVVZWpri4OHvMpk2blJ+fr2nTpikqKkqZmZlau3ZtpKcLAAAMFPHnwPQXPAcGGNh4DgwwMPXZc2AAAAB6GgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4/R4wKxcuVIOh0N33HGHve7EiRPKy8vTiBEjdOGFFyozM1MNDQ1h+9XV1SkjI0MXXHCBEhMTtXTpUrW3t/f0dAEAgAF6NGD27Nmjf/mXf9H3v//9sPWLFi3SCy+8oOeee067d+/WkSNHdOONN9rbOzo6lJGRoZMnT+r111/Xxo0bVVpaquXLl/fkdAEAgCF6LGBaWlqUlZWlJ598UsOHD7fXB4NBPfXUU3r00Uf14x//WJMmTdJvf/tbvf7663rjjTckSS+//LLeffdd/e53v9Oll16qmTNn6oEHHtC6det08uTJnpoyAAAwRI8FTF5enjIyMpSWlha2vqqqSm1tbWHrL774YiUnJ8vv90uS/H6/JkyYILfbbY9JT09XKBRSTU1Nl8drbW1VKBQKWwAAwMAU3RNvunnzZu3du1d79uw5Y1sgEFBMTIzi4+PD1rvdbgUCAXvMl+Pl1PZT27pSVFSk++67LwKzBwAA/V3EP4Gpr6/XP/7jP2rTpk2Ki4uL9NufVWFhoYLBoL3U19f32rEBAEDvinjAVFVVqbGxUT/4wQ8UHR2t6Oho7d69W2vXrlV0dLTcbrdOnjyp5ubmsP0aGhrk8XgkSR6P54xvJZ16fWrM6WJjY+V0OsMWAAAwMEU8YKZNm6b9+/erurraXiZPnqysrCz7fw8ePFg7duyw96mtrVVdXZ18Pp8kyefzaf/+/WpsbLTHlJeXy+l0KiUlJdJTBgAAhon4PTDDhg3T9773vbB1Q4cO1YgRI+z1OTk5Wrx4sRISEuR0OrVw4UL5fD5NmTJFkjR9+nSlpKRo3rx5Ki4uViAQ0N133628vDzFxsZGesoAAMAwPXIT79d57LHHFBUVpczMTLW2tio9PV1PPPGEvX3QoEHatm2bFixYIJ/Pp6FDhyo7O1v3339/X0wXAAD0Mw7Lsqy+nkRPCIVCcrlcCgaD3A8DDEBjCrb39RS67fDKjL6eAtDvnevPb/4tJAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABgn4gFTVFSkyy+/XMOGDVNiYqJmz56t2trasDEnTpxQXl6eRowYoQsvvFCZmZlqaGgIG1NXV6eMjAxdcMEFSkxM1NKlS9Xe3h7p6QIAAANFPGB2796tvLw8vfHGGyovL1dbW5umT5+u48eP22MWLVqkF154Qc8995x2796tI0eO6MYbb7S3d3R0KCMjQydPntTrr7+ujRs3qrS0VMuXL4/0dAEAgIEclmVZPXmATz/9VImJidq9e7euueYaBYNBfetb39LTTz+tm266SZJ08OBBjR8/Xn6/X1OmTNGLL76o6667TkeOHJHb7ZYklZSUaNmyZfr0008VExPztccNhUJyuVwKBoNyOp09eYoA+sCYgu19PYVuO7wyo6+nAPR75/rzu8fvgQkGg5KkhIQESVJVVZXa2tqUlpZmj7n44ouVnJwsv98vSfL7/ZowYYIdL5KUnp6uUCikmpqanp4yAADo56J78s07Ozt1xx136KqrrtL3vvc9SVIgEFBMTIzi4+PDxrrdbgUCAXvMl+Pl1PZT27rS2tqq1tZW+3UoFIrUaQAAgH6mRz+BycvL0zvvvKPNmzf35GEk/fnmYZfLZS9JSUk9fkwAANA3eixg8vPztW3bNu3atUvf+c537PUej0cnT55Uc3Nz2PiGhgZ5PB57zOnfSjr1+tSY0xUWFioYDNpLfX19BM8GAAD0JxEPGMuylJ+fry1btmjnzp0aO3Zs2PZJkyZp8ODB2rFjh72utrZWdXV18vl8kiSfz6f9+/ersbHRHlNeXi6n06mUlJQujxsbGyun0xm2AACAgSni98Dk5eXp6aef1n/9139p2LBh9j0rLpdLQ4YMkcvlUk5OjhYvXqyEhAQ5nU4tXLhQPp9PU6ZMkSRNnz5dKSkpmjdvnoqLixUIBHT33XcrLy9PsbGxkZ4yAAAwTMQDZv369ZKkH/3oR2Hrf/vb3+pnP/uZJOmxxx5TVFSUMjMz1draqvT0dD3xxBP22EGDBmnbtm1asGCBfD6fhg4dquzsbN1///2Rni4AADBQjz8Hpq/wHBhgYOM5MMDA1G+eAwMAABBpBAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACM068DZt26dRozZozi4uKUmpqqN998s6+nBAAA+oF+GzDPPPOMFi9erHvvvVd79+7VxIkTlZ6ersbGxr6eGgAA6GP9NmAeffRRzZ8/X7fddptSUlJUUlKiCy64QL/5zW/6emoAAKCPRff1BLpy8uRJVVVVqbCw0F4XFRWltLQ0+f3+LvdpbW1Va2ur/ToYDEqSQqFQz04WQJ/obP2ir6fQbfx9BHy9U/+dWJb1leP6ZcB89tln6ujokNvtDlvvdrt18ODBLvcpKirSfffdd8b6pKSkHpkjAHSXa3VfzwAwx7Fjx+Ryuc66vV8GzPkoLCzU4sWL7dednZ1qamrSiBEj5HA4+nBm/UMoFFJSUpLq6+vldDr7ejoDFte5d3CdewfXuXdwncNZlqVjx47J6/V+5bh+GTAjR47UoEGD1NDQELa+oaFBHo+ny31iY2MVGxsbti4+Pr6npmgsp9PJfyC9gOvcO7jOvYPr3Du4zv/vqz55OaVf3sQbExOjSZMmaceOHfa6zs5O7dixQz6frw9nBgAA+oN++QmMJC1evFjZ2dmaPHmyrrjiCq1evVrHjx/Xbbfd1tdTAwAAfazfBsxPf/pTffrpp1q+fLkCgYAuvfRSlZWVnXFjL85NbGys7r333jN+zYbI4jr3Dq5z7+A69w6u8/lxWF/3PSUAAIB+pl/eAwMAAPBVCBgAAGAcAgYAABiHgAEAAMYhYAaopqYmZWVlyel0Kj4+Xjk5OWppaTmnfS3L0syZM+VwOLR169aenegA0N1r3dTUpIULF2rcuHEaMmSIkpOTdfvtt9v/fhf+bN26dRozZozi4uKUmpqqN9988yvHP/fcc7r44osVFxenCRMm6L//+797aaZm6851fvLJJzV16lQNHz5cw4cPV1pa2tf+/4I/6+6f51M2b94sh8Oh2bNn9+wEDUTADFBZWVmqqalReXm5tm3bpoqKCuXm5p7TvqtXr+afX+iG7l7rI0eO6MiRI1q1apXeeecdlZaWqqysTDk5Ob046/7tmWee0eLFi3Xvvfdq7969mjhxotLT09XY2Njl+Ndff11z585VTk6O3n77bc2ePVuzZ8/WO++808szN0t3r/Mrr7yiuXPnateuXfL7/UpKStL06dP1ySef9PLMzdLd63zK4cOHdeedd2rq1Km9NFPDWBhw3n33XUuStWfPHnvdiy++aDkcDuuTTz75yn3ffvtt69vf/rZ19OhRS5K1ZcuWHp6t2b7Jtf6yZ5991oqJibHa2tp6YprGueKKK6y8vDz7dUdHh+X1eq2ioqIux998881WRkZG2LrU1FTrF7/4RY/O03Tdvc6na29vt4YNG2Zt3Lixp6Y4IJzPdW5vb7euvPJK61//9V+t7Oxs64YbbuiFmZqFT2AGIL/fr/j4eE2ePNlel5aWpqioKFVWVp51vy+++EK33HKL1q1bd9Z/cwrhzvdany4YDMrpdCo6ut8+W7LXnDx5UlVVVUpLS7PXRUVFKS0tTX6/v8t9/H5/2HhJSk9PP+t4nN91Pt0XX3yhtrY2JSQk9NQ0jXe+1/n+++9XYmIin8x+Bf62HIACgYASExPD1kVHRyshIUGBQOCs+y1atEhXXnmlbrjhhp6e4oBxvtf6yz777DM98MAD5/wrvoHus88+U0dHxxlP3Xa73Tp48GCX+wQCgS7Hn+v/B3+Jzuc6n27ZsmXyer1nxCP+3/lc51dffVVPPfWUqqure2GG5uITGIMUFBTI4XB85XKuf/Gc7vnnn9fOnTu1evXqyE7aUD15rb8sFAopIyNDKSkpWrFixTefONBLVq5cqc2bN2vLli2Ki4vr6+kMGMeOHdO8efP05JNPauTIkX09nX6NT2AMsmTJEv3sZz/7yjF/9Vd/JY/Hc8bNYe3t7Wpqajrrr4Z27typDz/8UPHx8WHrMzMzNXXqVL3yyivfYObm6clrfcqxY8c0Y8YMDRs2TFu2bNHgwYO/6bQHhJEjR2rQoEFqaGgIW9/Q0HDWa+rxeLo1Hud3nU9ZtWqVVq5cqd///vf6/ve/35PTNF53r/OHH36ow4cP6/rrr7fXdXZ2Svrzp7u1tbX67ne/27OTNkVf34SDyDt1Y+lbb71lr3vppZe+8sbSo0ePWvv37w9bJFlr1qyx/vjHP/bW1I1zPtfasiwrGAxaU6ZMsX74wx9ax48f742pGuWKK66w8vPz7dcdHR3Wt7/97a+8ife6664LW+fz+biJ92t09zpblmU9/PDDltPptPx+f29McUDoznX+05/+dMbfxTfccIP14x//2Nq/f7/V2tram1Pv1wiYAWrGjBnWZZddZlVWVlqvvvqqddFFF1lz5861t3/88cfWuHHjrMrKyrO+h/gW0jnp7rUOBoNWamqqNWHCBOuDDz6wjh49ai/t7e19dRr9yubNm63Y2FirtLTUevfdd63c3FwrPj7eCgQClmVZ1rx586yCggJ7/GuvvWZFR0dbq1atsg4cOGDde++91uDBg639+/f31SkYobvXeeXKlVZMTIz17//+72F/bo8dO9ZXp2CE7l7n0/EtpK4RMAPU//7v/1pz5861LrzwQsvpdFq33XZb2F8yhw4dsiRZu3btOut7EDDnprvXeteuXZakLpdDhw71zUn0Q48//riVnJxsxcTEWFdccYX1xhtv2Nt++MMfWtnZ2WHjn332Wetv/uZvrJiYGOuSSy6xtm/f3sszNlN3rvPo0aO7/HN777339v7EDdPdP89fRsB0zWFZltXbv7YCAAD4JvgWEgAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDj/B6wiHRQ+Q/fSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 41\n",
      "working on HD 1400 time is now 87\n",
      "out of 1538 total HDs, there were 1424.0 contiguous and 238.0 complement-contiguous HDs\n",
      "94 HDs had both discontiguity problems, while enclave-only = 1206 and discontig only= 20\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 114 1206\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 754880 district pop vs 794611 target\n",
      "we'll also trim any HD with total islands' pop <= 15892\n",
      "working on HD 0\n",
      "Out of 114 discontig HDs 114 had discontig HDs.\n",
      "Of these, 86 won't be under 754880 after all minor discontigys were shed, while 28 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 0\n",
      "There are 28 HDs still with both discontinuity problems\n",
      "Additionally, 77 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 6 0 seconds elapsed\n",
      "working on enclave-generating HD 766 1 seconds elapsed\n",
      "working on enclave-generating HD 1305 1 seconds elapsed\n",
      "Out of 1283 HDpolys that generated enclaves, 0 had contiguous adjrs, while 768 neighbored a state boundary while 515 had only internal enclaves\n"
     ]
    }
   ],
   "source": [
    "print(\"Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t,int(time.time()-startTime),\"seconds elapsed\")\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 834341 district pop vs 794611 target\n",
      "working on enclave-y HD 0 . We have evaluated 0 of 1283 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 318 . We have evaluated 200 of 1283 enclavy HDs.Time is now 20\n",
      "working on enclave-y HD 610 . We have evaluated 400 of 1283 enclavy HDs.Time is now 36\n",
      "working on enclave-y HD 826 . We have evaluated 600 of 1283 enclavy HDs.Time is now 50\n",
      "working on enclave-y HD 1028 . We have evaluated 800 of 1283 enclavy HDs.Time is now 64\n",
      "working on enclave-y HD 1237 . We have evaluated 1000 of 1283 enclavy HDs.Time is now 79\n",
      "working on enclave-y HD 1441 . We have evaluated 1200 of 1283 enclavy HDs.Time is now 95\n",
      "Of 1283 1149 wouldn't be over 834341 if all enclaves filled, while 134 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "targetList = list(set(internals + edgers + stillHasEnclave))\n",
    "for iii, t in enumerate(targetList):\n",
    "    if iii%200 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(targetList),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Of\",len(targetList),len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",\n",
    "      len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00402 0.1628\n",
      "CCB cluster 0 = unit 1457 with pop 66021.0 now has use of 1.0152586128060512\n",
      "CCB cluster 1 = unit 1458 with pop 16557.0 now has use of 1.0913222145501786\n",
      "CCB cluster 2 = unit 1459 with pop 47669.0 now has use of 0.8557982644764597\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 134 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "2c925cf4-ba57-49cd-9e88-6a27967003c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the original barredJettisonSet (usually CCB's) is {1457, 1458, 1459}\n",
      "1457 1.0152586128060512\n",
      "1458 1.0913222145501786\n",
      "1459 0.8557982644764597\n",
      "updated, original barredJettisonSet are {1457, 1459} {1457, 1458, 1459}\n"
     ]
    }
   ],
   "source": [
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "barredJettisonSet = set()\n",
    "for u in range(nUnits):\n",
    "    if abs(allUnits[u] - int(allUnits[u]) - 0.25) < 0.01:\n",
    "        barredJettisonSet.add(u)\n",
    "print(\"the original barredJettisonSet (usually CCB's) is\",barredJettisonSet)\n",
    "for u in barredJettisonSet:\n",
    "    print(u,unitUse[u])\n",
    "origBarredJettisonSet = set(list(barredJettisonSet))\n",
    "for u in origBarredJettisonSet:\n",
    "    if unitUse[u] > 1.04:\n",
    "        barredJettisonSet.remove(u)  #this one is overused in this state\n",
    "print(\"updated, original barredJettisonSet are\",barredJettisonSet, origBarredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the barredJettisonSet (cannot be shed in below MUSTFREE code) is [1457, 1459]\n",
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "print(\"the barredJettisonSet (cannot be shed in below MUSTFREE code) is\",barredJettisonSet)\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 134 HDs\n",
      "working on HD 73 cantFill 0 of 134 .Time is now 0\n",
      "working on HD 224 cantFill 20 of 134 .Time is now 0\n",
      "working on HD 383 cantFill 40 of 134 .Time is now 0\n",
      "working on HD 493 cantFill 60 of 134 .Time is now 0\n",
      "working on HD 714 cantFill 80 of 134 .Time is now 0\n",
      "working on HD 1214 cantFill 100 of 134 .Time is now 1\n",
      "working on HD 1437 cantFill 120 of 134 .Time is now 1\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.00198 0.16048\n",
      "CCB cluster 0 = unit 1457 with pop 66021.0 now has use of 1.0152586128060512\n",
      "CCB cluster 1 = unit 1458 with pop 16557.0 now has use of 0.9972812704240345\n",
      "CCB cluster 2 = unit 1459 with pop 47669.0 now has use of 0.8595233560726021\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66e91b98-63a9-47db-af63-039ac47b1835",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "out of 1538 total HDs, there were 1457 63 0 18 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 0\n",
      "working on current HD diam for HD number 1000\n",
      "here is the histogram of HD diameter = sqrt(area)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAlwUlEQVR4nO3df3CU9YHH8c8msAsBdnMBkk2G8EOxQIRAixpWe4oSEyBlZIxzYimkLYWT2ThC7hDS4UTwrqGcU/xxCM7cVbw7c1jbgmMQMEAJYwmIgQwRNCM5LDiwiZVjF+KxQPLcHzc804UE2JCw3w3v18wzwz7Pd3e/33ncydtnNxuHZVmWAAAADJIQ6wkAAABciUABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYJwesZ5AR7S2turkyZPq16+fHA5HrKcDAABugGVZOnv2rDIyMpSQcO1rJHEZKCdPnlRmZmaspwEAADrgxIkTGjRo0DXHxGWg9OvXT9L/L9Dtdsd4NgAA4EaEQiFlZmbaP8evJS4D5fLbOm63m0ABACDO3MjHM/iQLAAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjNMj1hMw0dAlm2M9hah9ubIg1lMAAKDTcAUFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCeqQFm7dq2ys7Pldrvldrvl8/m0ZcsW+/jEiRPlcDgitqeffjriMY4fP66CggIlJSUpNTVVixYt0qVLlzpnNQAAoFvoEc3gQYMGaeXKlbrrrrtkWZbeeustPfbYYzp48KDuvvtuSdLcuXO1YsUK+z5JSUn2v1taWlRQUCCv16s9e/bo1KlTmj17tnr27Klf/OIXnbQkAAAQ76IKlGnTpkXc/qd/+ietXbtWe/futQMlKSlJXq+3zft/+OGHOnLkiLZv3660tDSNGzdOL774ohYvXqwXXnhBTqezg8sAAADdSYc/g9LS0qINGzaoublZPp/P3v/2229rwIABGj16tEpLS/Xtt9/ax6qrqzVmzBilpaXZ+/Lz8xUKhXT48OF2nyscDisUCkVsAACg+4rqCook1dXVyefz6fz58+rbt682btyorKwsSdIPf/hDDRkyRBkZGTp06JAWL16s+vp6/f73v5ckBQKBiDiRZN8OBALtPmdZWZmWL18e7VQBAECcijpQRowYodraWgWDQf32t79VUVGRqqqqlJWVpXnz5tnjxowZo/T0dE2aNEkNDQ268847OzzJ0tJSlZSU2LdDoZAyMzM7/HgAAMBsUb/F43Q6NXz4cI0fP15lZWUaO3asXnnllTbH5uTkSJKOHj0qSfJ6vWpsbIwYc/l2e59bkSSXy2X/5tDlDQAAdF83/T0ora2tCofDbR6rra2VJKWnp0uSfD6f6urq1NTUZI+prKyU2+223yYCAACI6i2e0tJSTZkyRYMHD9bZs2dVXl6uXbt2adu2bWpoaFB5ebmmTp2q/v3769ChQ1q4cKEefPBBZWdnS5Ly8vKUlZWlWbNmadWqVQoEAlq6dKn8fr9cLleXLBAAAMSfqAKlqalJs2fP1qlTp+TxeJSdna1t27bp0Ucf1YkTJ7R9+3a9/PLLam5uVmZmpgoLC7V06VL7/omJiaqoqND8+fPl8/nUp08fFRUVRXxvCgAAgMOyLCvWk4hWKBSSx+NRMBjsks+jDF2yudMfs6t9ubIg1lMAAOCaovn5zd/iAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCeqQFm7dq2ys7Pldrvldrvl8/m0ZcsW+/j58+fl9/vVv39/9e3bV4WFhWpsbIx4jOPHj6ugoEBJSUlKTU3VokWLdOnSpc5ZDQAA6BaiCpRBgwZp5cqVqqmp0SeffKJHHnlEjz32mA4fPixJWrhwod5//329++67qqqq0smTJ/X444/b929paVFBQYEuXLigPXv26K233tL69ev1/PPPd+6qAABAXHNYlmXdzAOkpKTon//5n/XEE09o4MCBKi8v1xNPPCFJ+vzzzzVq1ChVV1drwoQJ2rJli37wgx/o5MmTSktLkyStW7dOixcv1tdffy2n03lDzxkKheTxeBQMBuV2u29m+m0aumRzpz9mV/tyZUGspwAAwDVF8/O7w59BaWlp0YYNG9Tc3Cyfz6eamhpdvHhRubm59piRI0dq8ODBqq6uliRVV1drzJgxdpxIUn5+vkKhkH0Vpi3hcFihUChiAwAA3VfUgVJXV6e+ffvK5XLp6aef1saNG5WVlaVAICCn06nk5OSI8WlpaQoEApKkQCAQESeXj18+1p6ysjJ5PB57y8zMjHbaAAAgjkQdKCNGjFBtba327dun+fPnq6ioSEeOHOmKudlKS0sVDAbt7cSJE136fAAAILZ6RHsHp9Op4cOHS5LGjx+v/fv365VXXtGTTz6pCxcu6MyZMxFXURobG+X1eiVJXq9XH3/8ccTjXf4tn8tj2uJyueRyuaKdKgAAiFM3/T0ora2tCofDGj9+vHr27KkdO3bYx+rr63X8+HH5fD5Jks/nU11dnZqamuwxlZWVcrvdysrKutmpAACAbiKqKyilpaWaMmWKBg8erLNnz6q8vFy7du3Stm3b5PF4NGfOHJWUlCglJUVut1vPPPOMfD6fJkyYIEnKy8tTVlaWZs2apVWrVikQCGjp0qXy+/1cIQEAALaoAqWpqUmzZ8/WqVOn5PF4lJ2drW3btunRRx+VJK1evVoJCQkqLCxUOBxWfn6+Xn/9dfv+iYmJqqio0Pz58+Xz+dSnTx8VFRVpxYoVnbsqAAAQ1276e1Bige9BuRrfgwIAMN0t+R4UAACArkKgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDhRBUpZWZnuvfde9evXT6mpqZo+fbrq6+sjxkycOFEOhyNie/rppyPGHD9+XAUFBUpKSlJqaqoWLVqkS5cu3fxqAABAt9AjmsFVVVXy+/269957denSJf385z9XXl6ejhw5oj59+tjj5s6dqxUrVti3k5KS7H+3tLSooKBAXq9Xe/bs0alTpzR79mz17NlTv/jFLzphSQAAIN5FFShbt26NuL1+/XqlpqaqpqZGDz74oL0/KSlJXq+3zcf48MMPdeTIEW3fvl1paWkaN26cXnzxRS1evFgvvPCCnE5nB5YBAAC6k5v6DEowGJQkpaSkROx/++23NWDAAI0ePVqlpaX69ttv7WPV1dUaM2aM0tLS7H35+fkKhUI6fPhwm88TDocVCoUiNgAA0H1FdQXlL7W2tmrBggV64IEHNHr0aHv/D3/4Qw0ZMkQZGRk6dOiQFi9erPr6ev3+97+XJAUCgYg4kWTfDgQCbT5XWVmZli9f3tGpAgCAONPhQPH7/fr000/10UcfReyfN2+e/e8xY8YoPT1dkyZNUkNDg+68884OPVdpaalKSkrs26FQSJmZmR2bOAAAMF6H3uIpLi5WRUWF/vCHP2jQoEHXHJuTkyNJOnr0qCTJ6/WqsbExYszl2+19bsXlcsntdkdsAACg+4oqUCzLUnFxsTZu3KidO3dq2LBh171PbW2tJCk9PV2S5PP5VFdXp6amJntMZWWl3G63srKyopkOAADopqJ6i8fv96u8vFzvvfee+vXrZ39mxOPxqHfv3mpoaFB5ebmmTp2q/v3769ChQ1q4cKEefPBBZWdnS5Ly8vKUlZWlWbNmadWqVQoEAlq6dKn8fr9cLlfnrxAAAMSdqK6grF27VsFgUBMnTlR6erq9vfPOO5Ikp9Op7du3Ky8vTyNHjtTf/d3fqbCwUO+//779GImJiaqoqFBiYqJ8Pp9+9KMfafbs2RHfmwIAAG5vUV1BsSzrmsczMzNVVVV13ccZMmSIPvjgg2ieGgAA3Eb4WzwAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDg9Yj0BAF1r6JLNsZ5C1L5cWRDrKQCIMa6gAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4UQVKWVmZ7r33XvXr10+pqamaPn266uvrI8acP39efr9f/fv3V9++fVVYWKjGxsaIMcePH1dBQYGSkpKUmpqqRYsW6dKlSze/GgAA0C1EFShVVVXy+/3au3evKisrdfHiReXl5am5udkes3DhQr3//vt69913VVVVpZMnT+rxxx+3j7e0tKigoEAXLlzQnj179NZbb2n9+vV6/vnnO29VAAAgrjksy7I6euevv/5aqampqqqq0oMPPqhgMKiBAweqvLxcTzzxhCTp888/16hRo1RdXa0JEyZoy5Yt+sEPfqCTJ08qLS1NkrRu3TotXrxYX3/9tZxO53WfNxQKyePxKBgMyu12d3T67Rq6ZHOnP2ZX+3JlQaynAEPx3zMAU0Tz8/umPoMSDAYlSSkpKZKkmpoaXbx4Ubm5ufaYkSNHavDgwaqurpYkVVdXa8yYMXacSFJ+fr5CoZAOHz7c5vOEw2GFQqGIDQAAdF8dDpTW1lYtWLBADzzwgEaPHi1JCgQCcjqdSk5OjhiblpamQCBgj/nLOLl8/PKxtpSVlcnj8dhbZmZmR6cNAADiQIcDxe/369NPP9WGDRs6cz5tKi0tVTAYtLcTJ050+XMCAIDY6dGROxUXF6uiokK7d+/WoEGD7P1er1cXLlzQmTNnIq6iNDY2yuv12mM+/vjjiMe7/Fs+l8dcyeVyyeVydWSqAAAgDkV1BcWyLBUXF2vjxo3auXOnhg0bFnF8/Pjx6tmzp3bs2GHvq6+v1/Hjx+Xz+SRJPp9PdXV1ampqssdUVlbK7XYrKyvrZtYCAAC6iaiuoPj9fpWXl+u9995Tv3797M+MeDwe9e7dWx6PR3PmzFFJSYlSUlLkdrv1zDPPyOfzacKECZKkvLw8ZWVladasWVq1apUCgYCWLl0qv9/PVRIAACApykBZu3atJGnixIkR+9988039+Mc/liStXr1aCQkJKiwsVDgcVn5+vl5//XV7bGJioioqKjR//nz5fD716dNHRUVFWrFixc2tBAAAdBtRBcqNfGVKr169tGbNGq1Zs6bdMUOGDNEHH3wQzVMDAIDbCH+LBwAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcXrEegIA0B0MXbI51lOI2pcrC2I9BaBdXEEBAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYJyoA2X37t2aNm2aMjIy5HA4tGnTpojjP/7xj+VwOCK2yZMnR4w5ffq0Zs6cKbfbreTkZM2ZM0fnzp27qYUAAIDuI+pAaW5u1tixY7VmzZp2x0yePFmnTp2yt//6r/+KOD5z5kwdPnxYlZWVqqio0O7duzVv3rzoZw8AALqlHtHeYcqUKZoyZco1x7hcLnm93jaPffbZZ9q6dav279+ve+65R5L02muvaerUqXrppZeUkZER7ZQAAEA30yWfQdm1a5dSU1M1YsQIzZ8/X9988419rLq6WsnJyXacSFJubq4SEhK0b9++rpgOAACIM1FfQbmeyZMn6/HHH9ewYcPU0NCgn//855oyZYqqq6uVmJioQCCg1NTUyEn06KGUlBQFAoE2HzMcDiscDtu3Q6FQZ08bAAAYpNMDZcaMGfa/x4wZo+zsbN15553atWuXJk2a1KHHLCsr0/LlyztrigAMN3TJ5lhPAUCMdfmvGd9xxx0aMGCAjh49Kknyer1qamqKGHPp0iWdPn263c+tlJaWKhgM2tuJEye6etoAACCGujxQvvrqK33zzTdKT0+XJPl8Pp05c0Y1NTX2mJ07d6q1tVU5OTltPobL5ZLb7Y7YAABA9xX1Wzznzp2zr4ZI0rFjx1RbW6uUlBSlpKRo+fLlKiwslNfrVUNDg5577jkNHz5c+fn5kqRRo0Zp8uTJmjt3rtatW6eLFy+quLhYM2bM4Dd4AACApA4EyieffKKHH37Yvl1SUiJJKioq0tq1a3Xo0CG99dZbOnPmjDIyMpSXl6cXX3xRLpfLvs/bb7+t4uJiTZo0SQkJCSosLNSrr77aCcsBAHRn8fj5pC9XFsR6CnEp6kCZOHGiLMtq9/i2bduu+xgpKSkqLy+P9qkBAMBtgr/FAwAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4PWI9ASCeDF2yOdZTAIDbAldQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxuGPBSJm+MN7AID2cAUFAAAYh0ABAADGIVAAAIBxog6U3bt3a9q0acrIyJDD4dCmTZsijluWpeeff17p6enq3bu3cnNz9cUXX0SMOX36tGbOnCm3263k5GTNmTNH586du6mFAACA7iPqQGlubtbYsWO1Zs2aNo+vWrVKr776qtatW6d9+/apT58+ys/P1/nz5+0xM2fO1OHDh1VZWamKigrt3r1b8+bN6/gqAABAtxL1b/FMmTJFU6ZMafOYZVl6+eWXtXTpUj322GOSpH//939XWlqaNm3apBkzZuizzz7T1q1btX//ft1zzz2SpNdee01Tp07VSy+9pIyMjJtYDgAA6A469TMox44dUyAQUG5urr3P4/EoJydH1dXVkqTq6molJyfbcSJJubm5SkhI0L59+9p83HA4rFAoFLEBAIDuq1MDJRAISJLS0tIi9qelpdnHAoGAUlNTI4736NFDKSkp9pgrlZWVyePx2FtmZmZnThsAABgmLn6Lp7S0VMFg0N5OnDgR6ykBAIAu1KnfJOv1eiVJjY2NSk9Pt/c3NjZq3Lhx9pimpqaI+126dEmnT5+2738ll8sll8vVmVMFAOCWiNdvzf5yZUFMn79Tr6AMGzZMXq9XO3bssPeFQiHt27dPPp9PkuTz+XTmzBnV1NTYY3bu3KnW1lbl5OR05nQAAECcivoKyrlz53T06FH79rFjx1RbW6uUlBQNHjxYCxYs0D/+4z/qrrvu0rBhw/QP//APysjI0PTp0yVJo0aN0uTJkzV37lytW7dOFy9eVHFxsWbMmMFv8AAAAEkdCJRPPvlEDz/8sH27pKREklRUVKT169frueeeU3Nzs+bNm6czZ87o+9//vrZu3apevXrZ93n77bdVXFysSZMmKSEhQYWFhXr11Vc7YTkAAKA7iDpQJk6cKMuy2j3ucDi0YsUKrVixot0xKSkpKi8vj/apAQDAbSIufosHAADcXggUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcaL+a8YAgO5h6JLNsZ4C0C6uoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADj9Ij1BNA5hi7ZHOspAADQaTr9CsoLL7wgh8MRsY0cOdI+fv78efn9fvXv3199+/ZVYWGhGhsbO3saAAAgjnXJWzx33323Tp06ZW8fffSRfWzhwoV6//339e6776qqqkonT57U448/3hXTAAAAcapL3uLp0aOHvF7vVfuDwaD+7d/+TeXl5XrkkUckSW+++aZGjRqlvXv3asKECV0xHQAAEGe65ArKF198oYyMDN1xxx2aOXOmjh8/LkmqqanRxYsXlZuba48dOXKkBg8erOrq6nYfLxwOKxQKRWwAAKD76vRAycnJ0fr167V161atXbtWx44d01//9V/r7NmzCgQCcjqdSk5OjrhPWlqaAoFAu49ZVlYmj8djb5mZmZ09bQAAYJBOf4tnypQp9r+zs7OVk5OjIUOG6De/+Y169+7doccsLS1VSUmJfTsUChEpAAB0Y13+PSjJycn6zne+o6NHj8rr9erChQs6c+ZMxJjGxsY2P7NymcvlktvtjtgAAED31eWBcu7cOTU0NCg9PV3jx49Xz549tWPHDvt4fX29jh8/Lp/P19VTAQAAcaLT3+L5+7//e02bNk1DhgzRyZMntWzZMiUmJuqpp56Sx+PRnDlzVFJSopSUFLndbj3zzDPy+Xz8Bg8AALB1eqB89dVXeuqpp/TNN99o4MCB+v73v6+9e/dq4MCBkqTVq1crISFBhYWFCofDys/P1+uvv97Z0wAAAHHMYVmWFetJRCsUCsnj8SgYDHbJ51H42ngAwO3uy5UFnf6Y0fz85o8FAgAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAME5MA2XNmjUaOnSoevXqpZycHH388cexnA4AADBEzALlnXfeUUlJiZYtW6YDBw5o7Nixys/PV1NTU6ymBAAADBGzQPnVr36luXPn6ic/+YmysrK0bt06JSUl6de//nWspgQAAAzRIxZPeuHCBdXU1Ki0tNTel5CQoNzcXFVXV181PhwOKxwO27eDwaAkKRQKdcn8WsPfdsnjAgAQL7riZ+zlx7Qs67pjYxIof/7zn9XS0qK0tLSI/Wlpafr888+vGl9WVqbly5dftT8zM7PL5ggAwO3M83LXPfbZs2fl8XiuOSYmgRKt0tJSlZSU2LdbW1t1+vRp9e/fXw6Ho1OfKxQKKTMzUydOnJDb7e7Ux4411hafWFt8Ym3xqzuvL9ZrsyxLZ8+eVUZGxnXHxiRQBgwYoMTERDU2Nkbsb2xslNfrvWq8y+WSy+WK2JecnNyVU5Tb7e52/2FextriE2uLT6wtfnXn9cVybde7cnJZTD4k63Q6NX78eO3YscPe19raqh07dsjn88ViSgAAwCAxe4unpKRERUVFuueee3Tffffp5ZdfVnNzs37yk5/EakoAAMAQMQuUJ598Ul9//bWef/55BQIBjRs3Tlu3br3qg7O3msvl0rJly656S6k7YG3xibXFJ9YWv7rz+uJpbQ7rRn7XBwAA4Bbib/EAAADjECgAAMA4BAoAADAOgQIAAIxzWwbKmjVrNHToUPXq1Us5OTn6+OOPrzn+3Xff1ciRI9WrVy+NGTNGH3zwwS2aafSiWdv69evlcDgitl69et3C2d6Y3bt3a9q0acrIyJDD4dCmTZuue59du3bpe9/7nlwul4YPH67169d3+Tw7Ktr17dq166rz5nA4FAgEbs2Eb1BZWZnuvfde9evXT6mpqZo+fbrq6+uve794eL11ZG3x8nqTpLVr1yo7O9v+Mi+fz6ctW7Zc8z7xcN6k6NcWT+ftL61cuVIOh0MLFiy45jiTz9ttFyjvvPOOSkpKtGzZMh04cEBjx45Vfn6+mpqa2hy/Z88ePfXUU5ozZ44OHjyo6dOna/r06fr0009v8cyvL9q1Sf//bYKnTp2ytz/96U+3cMY3prm5WWPHjtWaNWtuaPyxY8dUUFCghx9+WLW1tVqwYIF+9rOfadu2bV08046Jdn2X1dfXR5y71NTULpphx1RVVcnv92vv3r2qrKzUxYsXlZeXp+bm5nbvEy+vt46sTYqP15skDRo0SCtXrlRNTY0++eQTPfLII3rsscd0+PDhNsfHy3mTol+bFD/n7bL9+/frjTfeUHZ29jXHGX/erNvMfffdZ/n9fvt2S0uLlZGRYZWVlbU5/m/+5m+sgoKCiH05OTnW3/7t33bpPDsi2rW9+eablsfjuUWz6xySrI0bN15zzHPPPWfdfffdEfuefPJJKz8/vwtn1jluZH1/+MMfLEnW//zP/9ySOXWWpqYmS5JVVVXV7ph4er39pRtZWzy+3v7SX/3VX1n/+q//2uaxeD1vl11rbfF23s6ePWvdddddVmVlpfXQQw9Zzz77bLtjTT9vt9UVlAsXLqimpka5ubn2voSEBOXm5qq6urrN+1RXV0eMl6T8/Px2x8dKR9YmSefOndOQIUOUmZl53f+LiBfxcs5u1rhx45Senq5HH31Uf/zjH2M9nesKBoOSpJSUlHbHxOu5u5G1SfH5emtpadGGDRvU3Nzc7p8iidfzdiNrk+LrvPn9fhUUFFx1Ptpi+nm7rQLlz3/+s1paWq76ttq0tLR2378PBAJRjY+VjqxtxIgR+vWvf6333ntP//mf/6nW1lbdf//9+uqrr27FlLtMe+csFArpf//3f2M0q86Tnp6udevW6Xe/+51+97vfKTMzUxMnTtSBAwdiPbV2tba2asGCBXrggQc0evTodsfFy+vtL93o2uLt9VZXV6e+ffvK5XLp6aef1saNG5WVldXm2Hg7b9GsLZ7O24YNG3TgwAGVlZXd0HjTz1vMvuoesefz+SL+r+H+++/XqFGj9MYbb+jFF1+M4cxwLSNGjNCIESPs2/fff78aGhq0evVq/cd//EcMZ9Y+v9+vTz/9VB999FGsp9LpbnRt8fZ6GzFihGpraxUMBvXb3/5WRUVFqqqqavcHeTyJZm3xct5OnDihZ599VpWVlXHxId4bcVsFyoABA5SYmKjGxsaI/Y2NjfJ6vW3ex+v1RjU+Vjqytiv17NlT3/3ud3X06NGumOIt0945c7vd6t27d4xm1bXuu+8+Y3/4FxcXq6KiQrt379agQYOuOTZeXm+XRbO2K5n+enM6nRo+fLgkafz48dq/f79eeeUVvfHGG1eNjbfzFs3armTqeaupqVFTU5O+973v2ftaWlq0e/du/cu//IvC4bASExMj7mP6ebut3uJxOp0aP368duzYYe9rbW3Vjh072n3/0efzRYyXpMrKymu+XxkLHVnblVpaWlRXV6f09PSumuYtES/nrDPV1tYad94sy1JxcbE2btyonTt3atiwYde9T7ycu46s7Urx9nprbW1VOBxu81i8nLf2XGttVzL1vE2aNEl1dXWqra21t3vuuUczZ85UbW3tVXEixcF5i/WndG+1DRs2WC6Xy1q/fr115MgRa968eVZycrIVCAQsy7KsWbNmWUuWLLHH//GPf7R69OhhvfTSS9Znn31mLVu2zOrZs6dVV1cXqyW0K9q1LV++3Nq2bZvV0NBg1dTUWDNmzLB69eplHT58OFZLaNPZs2etgwcPWgcPHrQkWb/61a+sgwcPWn/6058sy7KsJUuWWLNmzbLH//d//7eVlJRkLVq0yPrss8+sNWvWWImJidbWrVtjtYRrinZ9q1evtjZt2mR98cUXVl1dnfXss89aCQkJ1vbt22O1hDbNnz/f8ng81q5du6xTp07Z27fffmuPidfXW0fWFi+vN8v6///mqqqqrGPHjlmHDh2ylixZYjkcDuvDDz+0LCt+z5tlRb+2eDpvV7ryt3ji7bzddoFiWZb12muvWYMHD7acTqd13333WXv37rWPPfTQQ1ZRUVHE+N/85jfWd77zHcvpdFp33323tXnz5ls84xsXzdoWLFhgj01LS7OmTp1qHThwIAazvrbLv1Z75XZ5LUVFRdZDDz101X3GjRtnOZ1O64477rDefPPNWz7vGxXt+n75y19ad955p9WrVy8rJSXFmjhxorVz587YTP4a2lqTpIhzEa+vt46sLV5eb5ZlWT/96U+tIUOGWE6n0xo4cKA1adIk+we4ZcXvebOs6NcWT+ftSlcGSrydN4dlWdatu14DAABwfbfVZ1AAAEB8IFAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAY5/8AP7lfIcjYsBwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "print(\"here is the histogram of HD diameter = sqrt(area)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than 39731 but less than 198652\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 81 HDs to triage in this block\n",
      "working on swell-filling discontig HD 2 our 1 th HD we think we can swell out of 81 0.0 sec elapsed\n",
      "working on swell-filling discontig HD 715 our 41 th HD we think we can swell out of 81 29.136 sec elapsed\n",
      "working on swell-filling discontig HD 1496 our 81 th HD we think we can swell out of 81 95.456 sec elapsed\n",
      "we partially regrew 81 HDs, leaving 0 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than\",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    "startTime = time.time()\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        SEC = r3(time.time()-startTime)\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList),SEC,\"sec elapsed\" )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.50 * aDP or contigPop > 2.*aDP - minPostFixPop:  #formerly < 0.75 aDP\n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 1.00198 0.16048\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00359 0.15411\n",
      "CCB cluster 0 = unit 1457 with pop 66021.0 now has use of 1.0215900100426039\n",
      "CCB cluster 1 = unit 1458 with pop 16557.0 now has use of 1.026303005997905\n",
      "CCB cluster 2 = unit 1459 with pop 47669.0 now has use of 0.8595233560726021\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1000 time is now 3\n",
      "out of 1538 total HDs, there were 1538.0 contiguous and 1535.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 3 and discontig only= 0\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 0 3\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "35c56235-eb85-49de-a253-48c9a08dbc31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in AZ, we just have three enclaveOnly to fix\n"
     ]
    }
   ],
   "source": [
    "print(\"in AZ, we just have three enclaveOnly to fix\")\n",
    "badDiscoList = eLgenerator.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "11e72640-80f1-44b4-b8aa-c8ee24718fd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 3\n",
      "swell-generating HD 927 our 1 th HD we must regrow out of 3 0 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 1.00359 0.15411\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00375 0.1529\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 500 time is now 1 sec\n",
      "working on HD 1000 time is now 2 sec\n",
      "working on HD 1500 time is now 4 sec\n",
      "out of 1538 total HDs, there were 1538 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 4 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f4bb76c7-0ae0-40cb-9c6a-ee436dfc30c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDunitList = [savedAgainUnitList[t].copy() for t in range(nHDs)]  #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "1.25067     0     9563 0.5\n",
      "1.02159     1457     66021 0.25\n",
      "1.0263     1458     16557 0.25\n",
      "0.85952     1459     47669 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )\n",
    "# note for MN, option 3 here led to tight CCB unit usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "9bc31b5a-992d-4a38-b9fe-3470b55e1e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #optional intermediate save\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6150cac-2249-415b-a364-edf55dc441ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00375 0.1529\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00375 0.1529\n"
     ]
    }
   ],
   "source": [
    "#for reboot, uncomment first line\n",
    "#HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ddf99687-37ad-4f81-9b78-b65614c6bb1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for squaring up\n",
      "Must have already established unitGeoms, pops, topology in above blocks\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./2024state_HD_output/WA7434opt3ContigButOffPopUnit.csv ./2024state_HD_output/WA7434opt3ContigButOffPopUnit.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of HDweight should be unity, actually is 0.9999999999998156\n",
      "I will now renormalize\n",
      "normalized weight is now 1.0\n",
      "I read in 7434 HD lists of units\n",
      "weighted unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WA - OPTIONAL restart here, pull in file*********************\n",
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for squaring up\") \n",
    "print(\"Must have already established unitGeoms, pops, topology in above blocks\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigButOffPopUnit.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse,weights = unitPop)\n",
    "print(\"weighted unit use histogram\")\n",
    "plt.show()\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "df4138f4-7e93-464a-b926-9291b5f68a04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 1\n",
      "working on avg unit-to-HDcp dist for HD 821\n",
      "working on avg unit-to-HDcp dist for HD 1627\n",
      "working on avg unit-to-HDcp dist for HD 2450\n",
      "working on avg unit-to-HDcp dist for HD 3253\n",
      "working on avg unit-to-HDcp dist for HD 4067\n",
      "working on avg unit-to-HDcp dist for HD 4882\n",
      "working on avg unit-to-HDcp dist for HD 5689\n",
      "working on avg unit-to-HDcp dist for HD 6496\n",
      "working on avg unit-to-HDcp dist for HD 7315\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 175 HDs with pop < 786665 to within 3717\n",
      "working on squaring up HD 6 time is now 0\n",
      "working on squaring up HD 555 time is now 10\n",
      "We have addressed underpop in a total of 175 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) ) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amped unit avg and sd usage are 1.00646 0.15104\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#prevUnitUse = [0.]*nUnits\n",
    "#for t in popHDlist:\n",
    "#    for u in latestHDlist[t]:\n",
    "#        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "#print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bb0be525-b8f4-4763-94e6-09fc491b833c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1457, 1458, 1459}\n"
     ]
    }
   ],
   "source": [
    "print (origBarredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ee512b2b-4daa-4a6b-9452-e8dfdfdebc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the current barredJettisonSet (usually CCB's) is {1457, 1459}\n",
      "1457 1.0215900100426039\n",
      "1459 0.8820123381074249\n"
     ]
    }
   ],
   "source": [
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "print(\"the current barredJettisonSet (usually CCB's) is\",barredJettisonSet)\n",
    "for u in barredJettisonSet:\n",
    "    print(u,unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "04ad620a-6c03-41ac-8ead-55c042b497f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "preFixList = [HDunitList[t].copy() for t in range(nHDs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7a231679-3bf7-46fa-aa05-10b5f7f2f560",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before we square down, add in that underused CCB where we can\n",
      "1459 was originally only used 0.8820123381074249\n",
      "we have 101 candidates for adding unit 1459\n",
      "5 loop, use now up to 0.9096623338705521\n",
      "10 loop, use now up to 0.9280272871349147\n",
      "15 loop, use now up to 0.9379579282785505\n",
      "20 loop, use now up to 0.9612724711535989\n",
      "25 loop, use now up to 0.9770764239456126\n",
      "30 loop, use now up to 0.992494024332216\n"
     ]
    }
   ],
   "source": [
    "print(\"before we square down, add in that underused CCB where we can\")\n",
    "canAddList, canAddScore = list(), list()\n",
    "underU = 1459\n",
    "print(underU,\"was originally only used\",unitUse[underU])\n",
    "for t in range(nHDs):\n",
    "    if underU not in HDunitList[t]:\n",
    "        neighbors = set(HDunitList[t]).intersection(set(unitNbrs[underU])) \n",
    "        if len(neighbors) > 0:  #underU is not in this HD, but adjoins it\n",
    "            contig,cContig, __, ___ = enclaveCheck(HDunitList[t] + [underU], unitNbrs)\n",
    "            if contig and cContig:\n",
    "                canAddList.append(t)\n",
    "                canAddScore.append(HDvPop[t] + unitPop[underU])\n",
    "loopNo = 0\n",
    "wasAdded = [False for t in canAddList]\n",
    "print(\"we have\",len(canAddList),\"candidates for adding unit\",underU)\n",
    "while loopNo < len(canAddList) and unitUse[underU] < 1.0:\n",
    "    loopNo += 1\n",
    "    currList, currScore = list(), list()\n",
    "    for i, t in enumerate(canAddList):\n",
    "        if not wasAdded[i]:\n",
    "            currList.append(canAddList[i])\n",
    "            currScore.append(canAddScore[i])\n",
    "    idx = currScore.index(np.min(currScore))\n",
    "    t = currList[idx]\n",
    "    wasAdded[canAddList.index(t)] = True\n",
    "    HDunitList[t].append(underU)\n",
    "    unitUse[underU] += nDistricts * HDweight[t]\n",
    "    if loopNo %5 == 0:\n",
    "        print(loopNo, \"loop, use now up to\",unitUse[underU])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "4b1b5e11-0969-48c1-860b-2dc6df9e53e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "just in case, did we create disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 500 time is now 1 sec\n",
      "working on HD 1000 time is now 3 sec\n",
      "working on HD 1500 time is now 4 sec\n",
      "out of 1538 total HDs, there were 1538 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 4 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"just in case, did we create disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "60321402-23d9-4c02-840d-9222e0d07311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1457, 1458, 1459}\n"
     ]
    }
   ],
   "source": [
    "print(origBarredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5a6cb84c-1d0f-4f02-831c-985fe4078dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1457, 1459} {1457, 1458, 1459}\n"
     ]
    }
   ],
   "source": [
    "#origBarredJettisonSet = set(list(barredJettisonSet))\n",
    "for u in origBarredJettisonSet:\n",
    "    if unitUse[u] > 1.04:\n",
    "        barredJettisonSet.remove(u)  #this one is overused in this state\n",
    "print(barredJettisonSet, origBarredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 332 overPopped HDs to within 3717\n",
      "working on squaring down HD 19 time is now 0\n",
      "working on squaring down HD 376 time is now 2\n",
      "working on squaring down HD 698 time is now 4\n",
      "working on squaring down HD 952 time is now 5\n",
      "working on squaring down HD 1207 time is now 7\n",
      "working on squaring down HD 1407 time is now 9\n",
      "We have addressed overpop in a total of 332 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN  #new for VA, the barred set is dynamic; we stop shedding upon underuse\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))  \n",
    "barredSet = set(list(barredJettisonSet))\n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    for u in origBarredJettisonSet:\n",
    "        if unitUse[u] < 1.00:\n",
    "            barredSet = barredSet.union({u})  #adding back in when become underused (new for VA)\n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        #barredSet = barredJettisonSet  #see above for VA-on modification\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5c4c929d-0208-4785-aa15-5681a493a530",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "just in case, did we create disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 500 time is now 1 sec\n",
      "working on HD 1000 time is now 2 sec\n",
      "working on HD 1500 time is now 4 sec\n",
      "out of 1538 total HDs, there were 1538 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 4 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"just in case, did we create disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.00646 0.15104\n",
      "shed unit avg and sd usage are 1.0029 0.14557\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 1538\n",
      "working on HD 600 out of 1538\n",
      "working on HD 1200 out of 1538\n",
      "all done checking HD and complement contiguity for all 1538 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 175\n",
      "overpop contigFail, enclaveFail = 0 0 out of 332\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 1457 with pop 66021.0 now has use 1.02159\n",
      "CCB unit 1458 with pop 16557.0 now has use 1.02292\n",
      "CCB unit 1459 with pop 47669.0 now has use 1.00321\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%600 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ff3288f1-f9ca-473c-886c-ee8d6c2637bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1538 1460\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, nUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ed668f8-bf1e-46eb-963f-cf5e2b935b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see MD code if we need to remove a stuck CCB's from any HDs and then redo the square-up after dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7a36c0ba-5a68-445b-a223-b7fa55dfd728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1538 HD centers\n"
     ]
    }
   ],
   "source": [
    "nHDs = len(vtdGeom)\n",
    "print(\"there are\",nHDs,\"HD centers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8014f0bf-74bb-40bc-b217-af4e204ea9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upon restart, need to pull in unit topology and data\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv ./state_map_files/MNunitTopologies_06Apr24.csv\n"
     ]
    }
   ],
   "source": [
    "#SKIP THE BELOW IF NOT A COLD RESTART\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "\n",
    "nbrListString = topDF[\"neighborList\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "unitParentVTDno = topDF[\"unitParentVTDno\"]  #NOTE: some units are VTD fragments, so they share a parentVTDno\n",
    "uVLstring = topDF[\"unitVTDlist\"]\n",
    "unitNbrs = [ast.literal_eval(uVLstring[u]) for u in range(nUnits)]\n",
    "allUnits = topDF[\"allUnits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "1457 1.02159 66021.0\n",
      "1458 1.02292 16557.0\n",
      "1459 1.00321 47669.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc95ca58-c27f-4ac9-b6f7-234a94945f07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.05\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 211 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 100\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 771 units out of 1460 with usage below 0.98\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 39730\n",
      "current avg and SD of unit usage are 1.0029 0.14557 . Now trying to increase up to 100 units' underusage\n",
      "all done trying to increase usage of unit 283 final usage = 0.98065 30 sec elapsed 49 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0029 0.1375\n",
      "all done trying to increase usage of unit 167 final usage = 0.7944 46 sec elapsed 16 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00296 0.13542\n",
      "all done trying to increase usage of unit 6 final usage = 0.92662 68 sec elapsed 66 0 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00296 0.13002\n",
      "all done trying to increase usage of unit 168 final usage = 0.77493 86 sec elapsed 8 1 successful, failed patches, couldn't start= 48\n",
      "current avg and SD of usage are 1.00297 0.12902\n",
      "all done trying to increase usage of unit 193 final usage = 0.98337 139 sec elapsed 69 0 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00267 0.12249\n",
      "all done trying to increase usage of unit 182 final usage = 0.84523 160 sec elapsed 14 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00266 0.12123\n",
      "all done trying to increase usage of unit 162 final usage = 0.98463 279 sec elapsed 79 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00267 0.11704\n"
     ]
    }
   ],
   "source": [
    "#EXCEPTION - for WA, run the overuse block first, then this one, as we have a right tail ***\n",
    "# for AZ, run down to 0.12, then to overuse, then back to under as needed\n",
    "\n",
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.05  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for uu in uuCandidates: #NOTE: pre 7/15/24, below addUseDist had \"u\" in place of uu's\n",
    "                if HDuuuCpop + unitPop[uu] <= maxExchangePop:\n",
    "                    addCandidates.append(uu)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.0029 1.00267 and their SDs are 0.14557 0.11704\n",
      "And here is the pop distro in state AZ\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 1538\n",
      "working on HD 500 out of 1538\n",
      "working on HD 1000 out of 1538\n",
      "working on HD 1500 out of 1538\n",
      "all done checking HD and complement contiguity for all 1538 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro in state\", STATE)\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1ee72cf0-618e-411f-aa37-518ac2c679ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"mostlyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.12\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 493 units out of 1460 with usage above 1.02\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 39730\n",
      "current avg and SD of unit usage are 1.00237 0.09732 . Now trying to reduce up to 493 units' overusage\n",
      "starting to reduce usage for unit 494 with usage 1.34472 . Total units tried = 1\n",
      "try to drop unit 494 from 30 th HD.  usage down to 1.1883230963229647\n",
      "try to drop unit 494 from 60 th HD.  usage down to 1.0415519704811482\n",
      "all done trying to reduce usage of unit 494 final usage = 1.01821 9 sec elapsed 48 0 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00225 0.0918\n",
      "starting to reduce usage for unit 611 with usage 1.34246 . Total units tried = 2\n",
      "try to drop unit 611 from 23 th HD.  usage down to 1.250168426157186\n",
      "try to drop unit 611 from 46 th HD.  usage down to 1.1664658696872279\n",
      "try to drop unit 611 from 69 th HD.  usage down to 1.0723833958236957\n",
      "try to drop unit 611 from 92 th HD.  usage down to 1.0243787948321843\n",
      "all done trying to reduce usage of unit 611 final usage = 1.01502 22 sec elapsed 48 0 successful, failed patches, couldn't start= 44\n",
      "current avg and SD of usage are 1.00213 0.08639\n",
      "starting to reduce usage for unit 323 with usage 1.31282 . Total units tried = 3\n",
      "try to drop unit 323 from 19 th HD.  usage down to 1.27604648645838\n",
      "try to drop unit 323 from 38 th HD.  usage down to 1.211164451887181\n",
      "try to drop unit 323 from 57 th HD.  usage down to 1.0850612920195029\n",
      "try to drop unit 323 from 76 th HD.  usage down to 1.0527448639460728\n",
      "all done trying to reduce usage of unit 323 final usage = 0.97861 31 sec elapsed 42 4 successful, failed patches, couldn't start= 39\n",
      "current avg and SD of usage are 1.00205 0.08088\n",
      "starting to reduce usage for unit 52 with usage 1.23175 . Total units tried = 4\n",
      "try to drop unit 52 from 22 th HD.  usage down to 1.1904612485600838\n",
      "try to drop unit 52 from 44 th HD.  usage down to 1.1769905119232207\n",
      "try to drop unit 52 from 66 th HD.  usage down to 1.1398062952369938\n",
      "try to drop unit 52 from 88 th HD.  usage down to 1.0989762709987287\n",
      "try to drop unit 52 from 110 th HD.  usage down to 1.034072842320392\n",
      "all done trying to reduce usage of unit 52 final usage = 1.02599 38 sec elapsed 39 0 successful, failed patches, couldn't start= 73\n",
      "current avg and SD of usage are 1.002 0.07856\n",
      "starting to reduce usage for unit 501 with usage 1.22332 . Total units tried = 5\n",
      "try to drop unit 501 from 29 th HD.  usage down to 1.1719842908524443\n",
      "try to drop unit 501 from 58 th HD.  usage down to 1.1267458220664464\n",
      "try to drop unit 501 from 87 th HD.  usage down to 1.08189748111653\n",
      "try to drop unit 501 from 116 th HD.  usage down to 1.0236740477734363\n",
      "all done trying to reduce usage of unit 501 final usage = 1.01667 48 sec elapsed 35 1 successful, failed patches, couldn't start= 81\n",
      "current avg and SD of usage are 1.00196 0.07564\n",
      "starting to reduce usage for unit 347 with usage 1.21485 . Total units tried = 6\n",
      "try to drop unit 347 from 15 th HD.  usage down to 1.1864391564177716\n",
      "try to drop unit 347 from 30 th HD.  usage down to 1.087239715517102\n",
      "try to drop unit 347 from 45 th HD.  usage down to 1.026294196659666\n",
      "all done trying to reduce usage of unit 347 final usage = 1.01836 52 sec elapsed 34 0 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.0019 0.07301\n",
      "starting to reduce usage for unit 881 with usage 1.21166 . Total units tried = 7\n",
      "try to drop unit 881 from 17 th HD.  usage down to 1.1452706018959413\n",
      "try to drop unit 881 from 34 th HD.  usage down to 1.1440322606356021\n",
      "try to drop unit 881 from 51 th HD.  usage down to 1.1420577103942684\n",
      "try to drop unit 881 from 68 th HD.  usage down to 1.1420577103942684\n",
      "try to drop unit 881 from 85 th HD.  usage down to 1.1420577103942684\n",
      "all done trying to reduce usage of unit 881 final usage = 1.14206 53 sec elapsed 12 0 successful, failed patches, couldn't start= 77\n",
      "current avg and SD of usage are 1.00187 0.07212\n",
      "starting to reduce usage for unit 53 with usage 1.19925 . Total units tried = 8\n",
      "try to drop unit 53 from 12 th HD.  usage down to 1.1134575645787446\n",
      "try to drop unit 53 from 24 th HD.  usage down to 1.051661315343268\n",
      "all done trying to reduce usage of unit 53 final usage = 1.01176 55 sec elapsed 27 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00177 0.06972\n",
      "starting to reduce usage for unit 321 with usage 1.20024 . Total units tried = 9\n",
      "try to drop unit 321 from 18 th HD.  usage down to 1.0920118598862274\n",
      "all done trying to reduce usage of unit 321 final usage = 1.00838 57 sec elapsed 22 0 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00177 0.06752\n",
      "starting to reduce usage for unit 479 with usage 1.18088 . Total units tried = 10\n",
      "try to drop unit 479 from 16 th HD.  usage down to 1.0585351161196428\n",
      "all done trying to reduce usage of unit 479 final usage = 1.01714 64 sec elapsed 22 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00173 0.06551\n",
      "starting to reduce usage for unit 772 with usage 1.17765 . Total units tried = 11\n",
      "try to drop unit 772 from 12 th HD.  usage down to 1.1218163680860322\n",
      "try to drop unit 772 from 24 th HD.  usage down to 1.0756617281236869\n",
      "try to drop unit 772 from 36 th HD.  usage down to 1.045845893631858\n",
      "try to drop unit 772 from 48 th HD.  usage down to 1.0324871614382574\n",
      "all done trying to reduce usage of unit 772 final usage = 1.01937 66 sec elapsed 43 2 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.0017 0.0637\n",
      "starting to reduce usage for unit 988 with usage 1.16965 . Total units tried = 12\n",
      "try to drop unit 988 from 17 th HD.  usage down to 1.0653220819906168\n",
      "all done trying to reduce usage of unit 988 final usage = 1.01182 70 sec elapsed 20 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00163 0.06153\n",
      "starting to reduce usage for unit 1246 with usage 1.1623 . Total units tried = 13\n",
      "try to drop unit 1246 from 15 th HD.  usage down to 1.1166163415741341\n",
      "try to drop unit 1246 from 30 th HD.  usage down to 1.1166163415741341\n",
      "try to drop unit 1246 from 45 th HD.  usage down to 1.104113373666133\n",
      "try to drop unit 1246 from 60 th HD.  usage down to 1.104113373666133\n",
      "try to drop unit 1246 from 75 th HD.  usage down to 1.094280893719963\n",
      "all done trying to reduce usage of unit 1246 final usage = 1.09428 72 sec elapsed 11 0 successful, failed patches, couldn't start= 64\n",
      "current avg and SD of usage are 1.00164 0.06076\n",
      "starting to reduce usage for unit 1175 with usage 1.15501 . Total units tried = 14\n",
      "try to drop unit 1175 from 28 th HD.  usage down to 1.0833094921877735\n",
      "try to drop unit 1175 from 56 th HD.  usage down to 1.0283744589597894\n",
      "try to drop unit 1175 from 84 th HD.  usage down to 1.0217422857463867\n",
      "all done trying to reduce usage of unit 1175 final usage = 1.01396 86 sec elapsed 27 2 successful, failed patches, couldn't start= 59\n",
      "current avg and SD of usage are 1.00163 0.05911\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run second for most states except MA, WA\n",
    "maxSD = 0.06 #0.06  #0.08  #0.04  #special 0.09 for WA, run first\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists) #work from farthest HD in\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d4ded333-828e-4ed3-84e2-4611cba052e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after overpatch, here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t]/aDP for t in popHDlist],weights = [HDweight[t] for t in popHDlist ],bins=50)\n",
    "print(\"after overpatch, here are your HD pops\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "31663998-9dd8-476a-89c1-d0dcf85e3a3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your CCB uses\n",
      "1457 66021 1.02159\n",
      "1458 16557 1.02071\n",
      "1459 47669 1.00321\n"
     ]
    }
   ],
   "source": [
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "print(\"here are your CCB uses\")\n",
    "for u in origBarredJettisonSet:\n",
    "    print(u,int(unitPop[u]),r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "6c6ba9e2-cd1a-4a0b-96b1-9497da14761f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.01\n"
     ]
    }
   ],
   "source": [
    "print(maxDistrictPop/aDP)\n",
    "maxDistrictPop = 1.005 * aDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3e81e694-1573-4bc4-bd0f-cd84e1e0d2dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looks like we have some over-popped somehow.  fix those\n",
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 203 overPopped HDs to within 3717\n",
      "working on squaring down HD 59 time is now 0\n",
      "working on squaring down HD 753 time is now 1\n",
      "working on squaring down HD 1043 time is now 2\n",
      "working on squaring down HD 1360 time is now 2\n",
      "We have addressed overpop in a total of 203 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looks like we have some over-popped somehow.  fix those\")\n",
    "#SQUARING DOWN  #new for VA, the barred set is dynamic; we stop shedding upon underuse\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))  \n",
    "barredSet = set(list(barredJettisonSet))\n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    for u in origBarredJettisonSet:\n",
    "        if unitUse[u] < 1.00:\n",
    "            barredSet = barredSet.union({u})  #adding back in when become underused (new for VA)\n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        #barredSet = barredJettisonSet  #see above for VA-on modification\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5f90c142-bbb4-47fc-833b-5f71060e5cf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.04\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 370 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 100\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.99 0.99\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 569 units out of 1460 with usage below 0.99\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 39730\n",
      "current avg and SD of unit usage are 1.00033 0.05891 . Now trying to increase up to 100 units' underusage\n",
      "all done trying to increase usage of unit 167 final usage = 0.95686 89 sec elapsed 65 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00033 0.05611\n",
      "all done trying to increase usage of unit 201 final usage = 0.99084 205 sec elapsed 44 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00032 0.0534\n",
      "all done trying to increase usage of unit 194 final usage = 0.99352 318 sec elapsed 39 0 successful, failed patches, couldn't start= 54\n",
      "current avg and SD of usage are 1.00033 0.05163\n",
      "all done trying to increase usage of unit 203 final usage = 0.995 400 sec elapsed 41 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00031 0.05006\n",
      "all done trying to increase usage of unit 1 final usage = 0.99015 409 sec elapsed 45 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00032 0.04598\n",
      "all done trying to increase usage of unit 168 final usage = 0.97296 509 sec elapsed 49 0 successful, failed patches, couldn't start= 56\n",
      "current avg and SD of usage are 1.00033 0.04442\n",
      "all done trying to increase usage of unit 226 final usage = 0.99249 595 sec elapsed 39 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00033 0.04211\n",
      "all done trying to increase usage of unit 12 final usage = 0.9964 609 sec elapsed 36 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00033 0.03874\n"
     ]
    }
   ],
   "source": [
    "#AZ-special: run under-patch to drive down to 0.04 after above overpatch\n",
    "# for AZ, run down to 0.12, then to overuse, then back to under as needed\n",
    "\n",
    "#FIRST = THIRD PATCHING BLOCK - boosting underuse.  (for AZ - after above under and over)\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.04  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.99\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for uu in uuCandidates: #NOTE: pre 7/15/24, below addUseDist had \"u\" in place of uu's\n",
    "                if HDuuuCpop + unitPop[uu] <= maxExchangePop:\n",
    "                    addCandidates.append(uu)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "a6a909ba-d2ea-4ed9-adaf-ded0fa73d071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGdCAYAAAA8F1jjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA63UlEQVR4nO3deXRU9f3/8ddkJROSDAmGLAbZFxWIoMT8UAHBsiithVZEqlAhyLdEhYgiIiJohVOXooh6ai3wbUHQfpFatLSySw2rpshSIBAEgRAJCSELSUju74+UKSELmTDLncnzcc49Zu79zL3vuRDnxed+7udaDMMwBAAAYEJ+ni4AAACgLgQVAABgWgQVAABgWgQVAABgWgQVAABgWgQVAABgWgQVAABgWgQVAABgWgGeLqAxKisrdfLkSYWFhclisXi6HAAA0ACGYej8+fOKi4uTn1/D+kq8MqicPHlSCQkJni4DAAA0wvHjx3X99dc3qK1XBpWwsDBJVR80PDzcw9UAAICGKCgoUEJCgv17vCG8MqhcutwTHh5OUAEAwMs4MmyDwbQAAMC0CCoAAMC0CCoAAMC0vHKMCgDA+xiGoYsXL6qiosLTpcBF/P39FRAQ4NSpQwgqAACXKysr06lTp1RcXOzpUuBiVqtVsbGxCgoKcsr+CCoAAJeqrKxUVlaW/P39FRcXp6CgICbr9EGGYaisrEw//PCDsrKy1LFjxwZP6lYfggoAwKXKyspUWVmphIQEWa1WT5cDFwoJCVFgYKC+++47lZWVqVmzZte8TwbTAgDcwhn/uob5OfvPmb81AADAtLj0AwDwiBP5JcorKnPb8VqEBineFuK24zXW0aNH1bZtW33zzTdKTEx06r7btGmjyZMna/LkyU7drysRVAAAbnciv0QDX9+kknL33aocEuivtU/1dWtY2bhxo/r376+8vDzZbDa3HdeXEFQAAG6XV1SmkvIKzR+ZqA7RzV1+vMycQk1ekaG8ojKv6FXBfzFGBQDgMR2im+vm+AiXL40NQ/369VNqaqpSU1MVERGhli1baubMmTIMQ5L0xz/+UbfeeqvCwsIUExOjhx56SDk5OZKqLuH0799fktSiRQtZLBaNHTtWUtUt27/5zW/UoUMHBQcHq3Xr1vr1r39d7dhHjhxR//79ZbVa1aNHD6Wnp1fbvmXLFt15550KCQlRQkKCnnjiCRUVFdm35+TkaNiwYQoJCVHbtm21dOnSRp0DTyOoAG52Ir9Ee06c04n8Ek+XAqABlixZooCAAG3fvl1vvvmm3njjDf3+97+XJJWXl+ull17Sv/71L61atUpHjx61h5GEhAT93//9nyTpwIEDOnXqlN58801J0vTp0zVv3jzNnDlT+/bt07Jly9SqVatqx50xY4amTp2qjIwMderUSaNGjdLFixclSYcPH9bgwYM1YsQI7d69WytWrNCWLVuUmppqf//YsWN1/PhxbdiwQX/+85/1zjvv2EOUVzG80Llz5wxJxrlz5zxdCuCQ7/OKjS7P/824Ydpqo8vzfzO+zyv2dEmAy5WUlBj79u0zSkpK7Ou+/T7fuGHaauPb7/PdUkNjj9e3b1+ja9euRmVlpX3dtGnTjK5du9bafseOHYYk4/z584ZhGMaGDRsMSUZeXp69TUFBgREcHGy8//77te4jKyvLkGT8/ve/t6/bu3evIcnYv3+/YRiGMW7cOGPChAnV3vfll18afn5+RklJiXHgwAFDkrF9+3b79v379xuSjN/+9rcOnQNH1fbnfUljvr/pUQHc6NJ1+cfv7qCS8gq33vEAoHFuv/32ajPpJicn69ChQ6qoqNCuXbs0bNgwtW7dWmFhYerbt68k6dixY3Xub//+/SotLdWAAQPqPW737t3tP8fGxkqSvUfkX//6lxYvXqzmzZvbl0GDBtlnAd6/f78CAgLUq1cv+z66dOnilQN6HQ4qmzdv1rBhwxQXFyeLxaJVq1ZV226xWGpdXn31VXubNm3a1Ng+b968a/4wgLdgMB/g/S5cuKBBgwYpPDxcS5cu1Y4dO/TJJ59IqpqNty4hIQ37/Q8MDLT/fCkoVVZWSpIKCwv12GOPKSMjw77861//0qFDh9S+ffvGfiRTcviun6KiIvXo0UOPPvqohg8fXmP7qVOnqr3+29/+pnHjxmnEiBHV1s+ZM0cpKSn212FhYY6WAgCAy23btq3a661bt6pjx47697//rdzcXM2bN08JCQmSpJ07d1Zre+nBfJc/Mbpjx44KCQnRunXrNH78+EbV1LNnT+3bt08dOnSodXuXLl108eJF7dq1S7fddpukqnEy+fn5jTqeJzkcVIYMGaIhQ4bUuT0mJqba67/85S/q37+/2rVrV239pRHSAACY2bFjx5SWlqbHHntMX3/9tRYsWKDXX39drVu3VlBQkBYsWKCJEydqz549eumll6q994YbbpDFYtHq1as1dOhQhYSEqHnz5po2bZqeeeYZBQUFqU+fPvrhhx+0d+9ejRs3rkE1TZs2TbfffrtSU1M1fvx4hYaGat++ffriiy/09ttvq3Pnzho8eLAee+wxvfvuuwoICNDkyZMb3JtjJi6dR+X06dP67LPPtGTJkhrb5s2bp5deekmtW7fWQw89pClTpiggoPZySktLVVpaan9dUFDgspoBAO6TmVNo+uM88sgjKikpUe/eveXv768nn3xSEyZMkMVi0eLFi/Xcc8/prbfeUs+ePfXaa6/pxz/+sf298fHxmj17tp599ln98pe/1COPPKLFixdr5syZCggI0AsvvKCTJ08qNjZWEydObHBN3bt316ZNmzRjxgzdeeedMgxD7du318iRI+1tFi1apPHjx6tv375q1aqVXn75Zc2cObPR58FTXBpUlixZorCwsBqXiJ544gn17NlTkZGR+uqrrzR9+nSdOnVKb7zxRq37mTt3rmbPnu3KUgEAbtQiNEghgf6avCLDbccMCfRXi9Agh98XGBio+fPn6913362xbdSoURo1alS1dcZ/5li5ZObMmTUCgp+fn2bMmKEZM2bU2GebNm1q7MNms9VYd9ttt+kf//hHnXXHxMRo9erV1dY9/PDDdbY3K5cGlT/84Q8aPXp0jcc8p6Wl2X/u3r27goKC9Nhjj2nu3LkKDg6usZ/p06dXe09BQYH9eiAAwPvE20K09qm+POsHV+WyoPLll1/qwIEDWrFixVXbJiUl6eLFizp69Kg6d+5cY3twcHCtAQYA4L3ibSEEB1yVy4LKBx98oF69eqlHjx5XbZuRkSE/Pz9FR0e7qhwAABy2ceNGT5fQ5DkcVAoLC5WZmWl/nZWVpYyMDEVGRqp169aSqi7NfPzxx3r99ddrvD89PV3btm1T//79FRYWpvT0dE2ZMkW/+MUv1KJFi2v4KAAAwNc4HFR27txpf8iS9N/xJmPGjNHixYslScuXL5dhGDUGGElVl3GWL1+uF198UaWlpWrbtq2mTJlSbQwKAACA1Iig0q9fvxojj680YcIETZgwodZtPXv21NatWx09LAAAaIJ41g8AADAtggoAADAtggoAADAtl074BgBAnfKPS8W57jueNUqyNY3JQseOHav8/HytWrXKqftdvHixJk+e7NaHGxJUAADul39cWthbKi923zEDrdKk7V4XVvr166fExETNnz/f06V4BEEFAOB+xblVIWX4+1LLTq4/3pmD0sqUquN6WVBp6hijAgDwnJadpLhE1y+NCENt2rSp0YuRmJioF198UZJksVj0+9//Xj/96U9ltVrVsWNHffrpp/a2GzdulMVi0Weffabu3burWbNmuv3227Vnzx57m9zcXI0aNUrx8fGyWq3q1q2bPvzwQ/v2sWPHatOmTXrzzTdlsVhksVh09OhRSdLevXt13333KTw8XGFhYbrzzjt1+PDhavW+9tprio2NVVRUlCZNmqTy8nL7ttLSUk2dOlXx8fEKDQ1VUlJSjZl4Fy9erNatW8tqteqnP/2pcnPdeKnuPwgqAAA00uzZs/XAAw9o9+7dGjp0qEaPHq2zZ89Wa/P000/r9ddf144dO3Tddddp2LBh9sBw4cIF9erVS5999pn27NmjCRMm6OGHH9b27dslSW+++aaSk5OVkpKiU6dO6dSpU0pISNCJEyd01113KTg4WOvXr9euXbv06KOP6uLFi/bjbtiwQYcPH9aGDRu0ZMkSLV682D4xqySlpqYqPT1dy5cv1+7du/Xzn/9cgwcP1qFDhyRJ27Zt07hx45SamqqMjAz1799fL7/8sovPaE1c+gEAoJHGjh1rn4X9lVde0VtvvaXt27dr8ODB9jazZs3SPffcI0lasmSJrr/+en3yySd64IEHFB8fr6lTp9rbPv744/r73/+ujz76SL1791ZERISCgoJktVoVExNjb7dw4UJFRERo+fLlCgwMlCR16lS916hFixZ6++235e/vry5duujee+/VunXrlJKSomPHjmnRokU6duyY4uLiJElTp07VmjVrtGjRIr3yyit68803NXjwYD3zzDP2/X/11Vdas2aNC85k3QgqAAA0Uvfu3e0/h4aGKjw8XDk5OdXaJCcn23+OjIxU586dtX//fklSRUWFXnnlFX300Uc6ceKEysrKVFpaKqvVWu9xMzIydOedd9pDSm1uuukm+fv721/Hxsbq22+/lSR9++23qqioqBFuSktLFRUVJUnav3+/fvrTn9b4LAQVAABMwM/Pr8YjYy4f4yGpRlCwWCyqrKxs8DFeffVVvfnmm5o/f766deum0NBQTZ48WWVlZfW+LyQk5Kr7rq+2wsJC+fv7a9euXdXCjCQ1b968wfW7A0EFAIBaXHfddTp16pT9dUFBgbKyshzez9atW9W6dWtJUl5eng4ePKiuXbtKkv75z3/qJz/5iX7xi19IkiorK3Xw4EHdeOON9vcHBQWpoqKi2j67d++uJUuWqLy8vN5elbrccsstqqioUE5Oju68885a23Tt2lXbtm2r8VncjcG0AADU4u6779Yf//hHffnll/r22281ZsyYGr0PDTFnzhytW7dOe/bs0dixY9WyZUvdf//9kqSOHTvqiy++0FdffaX9+/frscce0+nTp6u9v02bNtq2bZuOHj2qM2fOqLKyUqmpqSooKNCDDz6onTt36tChQ/rjH/+oAwcONKimTp06afTo0XrkkUe0cuVKZWVlafv27Zo7d64+++wzSdITTzyhNWvW6LXXXtOhQ4f09ttvu/2yj0SPCgDAk84cNO1xpk+frqysLN13332KiIjQSy+91KgelXnz5unJJ5/UoUOHlJiYqL/+9a8KCgqSJD3//PM6cuSIBg0aJKvVqgkTJuj+++/XuXPn7O+fOnWqxowZoxtvvFElJSXKyspSmzZttH79ej399NPq27ev/P39lZiYqD59+jS4rkWLFunll1/WU089pRMnTqhly5a6/fbbdd9990mSbr/9dr3//vuaNWuWXnjhBQ0cOFDPP/+8XnrpJYfPwbWwGFdegPMCBQUFioiI0Llz5xQeHu7pcoAG23PinO5bsEXzhnfTsyu/1erH79DN8RGeLgtwqQsXLigrK0tt27ZVs2bNqlY2gZlpN27cqP79+ysvL082m80txzSDWv+8/6Mx39/0qAAA3M+WUBUaeNYProKgAgDwDFsCwQFXRVABAMAF+vXrV+P2ZjiOoAK42In8EuUVlalFaJCnSwEAr0NQAVzoRH6JBr6+SSXlFQoJ9Nf8BxM9XRIAeBXmUQFcKK+oTCXlFXr87g4qKa9QXlH9s00CvozLIE2Ds/+cCSqAG8Tbrj7dNeCrLs2cWlzsxluR4TGX/pwbM2Nubbj0AwBwKX9/f9lsNvvD+qxWqywWi4ergrMZhqHi4mLl5OTIZrM1ahbf2hBUAAAuFxMTI0k1niwM32Oz2ex/3s5AUAEAuJzFYlFsbKyio6NrPIEYviMwMNBpPSmXEFQAAG7j7+/v9C8y+DYG0wIAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANMiqAAAANNyOKhs3rxZw4YNU1xcnCwWi1atWlVt+9ixY2WxWKotgwcPrtbm7NmzGj16tMLDw2Wz2TRu3DgVFhZe0wcBAAC+x+GgUlRUpB49emjhwoV1thk8eLBOnTplXz788MNq20ePHq29e/fqiy++0OrVq7V582ZNmDDB8eoBAIBPC3D0DUOGDNGQIUPqbRMcHKyYmJhat+3fv19r1qzRjh07dOutt0qSFixYoKFDh+q1115TXFycoyUBAAAf5ZIxKhs3blR0dLQ6d+6s//mf/1Fubq59W3p6umw2mz2kSNLAgQPl5+enbdu2uaIcAADgpRzuUbmawYMHa/jw4Wrbtq0OHz6s5557TkOGDFF6err8/f2VnZ2t6Ojo6kUEBCgyMlLZ2dm17rO0tFSlpaX21wUFBc4uGwAAmJDTg8qDDz5o/7lbt27q3r272rdvr40bN2rAgAGN2ufcuXM1e/ZsZ5UIAAC8hMtvT27Xrp1atmypzMxMSVJMTIxycnKqtbl48aLOnj1b57iW6dOn69y5c/bl+PHjri4bAACYgMuDyvfff6/c3FzFxsZKkpKTk5Wfn69du3bZ26xfv16VlZVKSkqqdR/BwcEKDw+vtgAAAN/n8KWfwsJCe++IJGVlZSkjI0ORkZGKjIzU7NmzNWLECMXExOjw4cN65pln1KFDBw0aNEiS1LVrVw0ePFgpKSl67733VF5ertTUVD344IPc8QMAAKpxuEdl586duuWWW3TLLbdIktLS0nTLLbfohRdekL+/v3bv3q0f//jH6tSpk8aNG6devXrpyy+/VHBwsH0fS5cuVZcuXTRgwAANHTpUd9xxh373u98571MBAACf4HCPSr9+/WQYRp3b//73v191H5GRkVq2bJmjhwYAAE0Mz/oBAACmRVABAACmRVABAACm5fQJ3wCvlX9cKs6tv401SrIluKceAABBBZBUFVIW9pbKi+tvF2iVJm0nrACAmxBUAKmqJ6W8WBr+vtSyU+1tzhyUVqZUtSWoAIBbEFSAy7XsJMUleroKAMB/MJgWAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYVoCnCwDcIv+4VJxb9/YzB91XCwCgwQgq8H35x6WFvaXy4vrbBVola5R7agIANAhBBb6vOLcqpAx/X2rZqe521ijJluC+ugAAV0VQQdPRspMUl+jpKgAADmAwLQAAMC2CCgAAMC2CCgAAMC2CCgAAMC2CCgAAMC2CCgAAMC2CCgAAMC2CCgAAMC0mfAMcdbXnAjkww21mTqEkqUVokOJtIddaGQD4HId7VDZv3qxhw4YpLi5OFotFq1atsm8rLy/XtGnT1K1bN4WGhiouLk6PPPKITp48WW0fbdq0kcViqbbMmzfvmj8M4FLWqKrnAa1MkX7Xt+5lYe+q5wvVo0VokEIC/TV5RYbuW7BFA1/fpBP5JW76IADgPRzuUSkqKlKPHj306KOPavjw4dW2FRcX6+uvv9bMmTPVo0cP5eXl6cknn9SPf/xj7dy5s1rbOXPmKCUlxf46LCyskR8BcBNbgjRp+9WfwrwypapNPb0q8bYQrX2qr/KKypSZU6jJKzKUV1RGrwoAXMHhoDJkyBANGTKk1m0RERH64osvqq17++231bt3bx07dkytW7e2rw8LC1NMTIyjhwc8y5bgtAcXxttCCCYAcBUuH0x77tw5WSwW2Wy2auvnzZunqKgo3XLLLXr11Vd18eLFOvdRWlqqgoKCagsAAPB9Lh1Me+HCBU2bNk2jRo1SeHi4ff0TTzyhnj17KjIyUl999ZWmT5+uU6dO6Y033qh1P3PnztXs2bNdWSoAADAhlwWV8vJyPfDAAzIMQ++++261bWlpafafu3fvrqCgID322GOaO3eugoODa+xr+vTp1d5TUFCghATndL8DAADzcklQuRRSvvvuO61fv75ab0ptkpKSdPHiRR09elSdO3eusT04OLjWAAMAAHyb04PKpZBy6NAhbdiwQVFRUVd9T0ZGhvz8/BQdHe3scgAAgBdzOKgUFhYqMzPT/jorK0sZGRmKjIxUbGysfvazn+nrr7/W6tWrVVFRoezsbElSZGSkgoKClJ6erm3btql///4KCwtTenq6pkyZol/84hdq0aKF8z4ZAADweg4HlZ07d6p///7215fGjowZM0YvvviiPv30U0lSYmJitfdt2LBB/fr1U3BwsJYvX64XX3xRpaWlatu2raZMmVJtDArgkPzjV5/bBADglRwOKv369ZNhGHVur2+bJPXs2VNbt2519LBA7fKPV80EW15cf7tAa9XMsgAAr8KzfuDdinOrQsrw96WWnepu58DzdwAA5kFQgW9o2UmKS/R0FQAAJ3P5zLQAAACNRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmFeDpAgCfdOagJKnZmULdZMlSi3P+1f4bWNhWUoRnawQAL0BQAZzJGiUFWqWVKZKkDpI+C5b0T2nQZf+t/DhESt0h2RI8WS0AmB5BBXAmW4I0abtUnCtJyvyhUE8uz9ATd3fQW+sz9cTdHfT5hk16U+9Ix9Kl4lx7r0uzMxGSpfl/92WNIsgAaPIIKoCz2RLsAeOCcU57jXPKi7hRe40K5UXcqB2Vp1UZECK/K3tdPrliP4HWqtBDWAHQhBFUADc7qZY69PP16hxWJum/vS5vPpioDtf9p0flzMGqy0fFuQQVAE0aQQXwgPLm8VJc1WDaS70uF1p2s68DAFTh9mQAAGBaBBUAAGBaBBUAAGBaBBUAAGBaBBUAAGBaBBUAAGBaDgeVzZs3a9iwYYqLi5PFYtGqVauqbTcMQy+88IJiY2MVEhKigQMH6tChQ9XanD17VqNHj1Z4eLhsNpvGjRunwsLCa/ogAADA9zgcVIqKitSjRw8tXLiw1u2/+c1v9NZbb+m9997Ttm3bFBoaqkGDBunChQv2NqNHj9bevXv1xRdfaPXq1dq8ebMmTJjQ+E8BAAB8ksMTvg0ZMkRDhgypdZthGJo/f76ef/55/eQnP5Ek/e///q9atWqlVatW6cEHH9T+/fu1Zs0a7dixQ7feeqskacGCBRo6dKhee+01xcXFXcPHAQAAvsSpY1SysrKUnZ2tgQMH2tdFREQoKSlJ6enpkqT09HTZbDZ7SJGkgQMHys/PT9u2bXNmOQAAwMs5dQr97OxsSVKrVq2qrW/VqpV9W3Z2tqKjo6sXERCgyMhIe5srlZaWqrS01P66oKDAmWUDAACT8oq7fubOnauIiAj7kpDAQ9oAAGgKnBpUYmJiJEmnT5+utv706dP2bTExMcrJyam2/eLFizp79qy9zZWmT5+uc+fO2Zfjx487s2zAbU7kl3i6BADwKk4NKm3btlVMTIzWrVtnX1dQUKBt27YpOTlZkpScnKz8/Hzt2rXL3mb9+vWqrKxUUlJSrfsNDg5WeHh4tQXwJi1CgxQS6K8F6zMVEuivFqFBni4JALyCw2NUCgsLlZmZaX+dlZWljIwMRUZGqnXr1po8ebJefvlldezYUW3bttXMmTMVFxen+++/X5LUtWtXDR48WCkpKXrvvfdUXl6u1NRUPfjgg9zxA58VbwvR2qf6Kq+oTC1CgxRvC/F0SQDgFRwOKjt37lT//v3tr9PS0iRJY8aM0eLFi/XMM8+oqKhIEyZMUH5+vu644w6tWbNGzZo1s79n6dKlSk1N1YABA+Tn56cRI0borbfecsLHAcwr3hZCQAEABzkcVPr16yfDMOrcbrFYNGfOHM2ZM6fONpGRkVq2bJmjhwYAAE2MV9z1AwAAmiaCCgAAMC2CCgAAMC2CCgAAMC2CCgAAMC2CCgAAMC2nPpQQcLr841Jxbt3bzxx0Xy0AALcjqMC88o9LC3tL5cX1twu0StYo99QEAHArggrMqzi3KqQMf19q2anudtYoycYTtQHAFxFUYH4tO0lxiZ6uAgDgAQymBQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAApkVQAQAAphXg6QIAX3Qiv0R5RWXKzCn0dCkA4NUIKoCTncgv0cDXN6mkvEKSFBLorxahQR6uCgC8E0EFcLK8ojKVlFdo/shEdYhurhahQYq3hXi6LADwSgQVwEU6RDfXzfERni4DALwag2kBAIBpEVQAAIBpEVQAAIBpOT2otGnTRhaLpcYyadIkSVK/fv1qbJs4caKzywAAAD7A6YNpd+zYoYqKCvvrPXv26J577tHPf/5z+7qUlBTNmTPH/tpqtTq7DAAA4AOcHlSuu+66aq/nzZun9u3bq2/fvvZ1VqtVMTExzj40AADwMS4do1JWVqY//elPevTRR2WxWOzrly5dqpYtW+rmm2/W9OnTVVxcXO9+SktLVVBQUG0BAAC+z6XzqKxatUr5+fkaO3asfd1DDz2kG264QXFxcdq9e7emTZumAwcOaOXKlXXuZ+7cuZo9e7YrSwUAACbk0qDywQcfaMiQIYqLi7OvmzBhgv3nbt26KTY2VgMGDNDhw4fVvn37Wvczffp0paWl2V8XFBQoISHBdYUDZnHmYP3brVGSjd8FAL7LZUHlu+++09q1a+vtKZGkpKQkSVJmZmadQSU4OFjBwcFOrxEwLWuUFGiVVqbU3y7QKk3aTlgB4LNcFlQWLVqk6Oho3XvvvfW2y8jIkCTFxsa6qhTA+9gSqgJIcW7dbc4crAoyxbkEFQA+yyVBpbKyUosWLdKYMWMUEPDfQxw+fFjLli3T0KFDFRUVpd27d2vKlCm666671L17d1eUAngvWwIBBECT55KgsnbtWh07dkyPPvpotfVBQUFau3at5s+fr6KiIiUkJGjEiBF6/vnnXVEGAADwci4JKj/60Y9kGEaN9QkJCdq0aZMrDgkAAHwQz/oBAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABAACmRVABTCIzp1An8ks8XQYAmApBBfCwFqFBCgn01+QVGRr4+ibCCgBchqACeFi8LURrn+qr+SMTVVJeobyiMk+XBACmEeDpAgBUhZW86OaeLgMATIceFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoEFQAAYFoBni4APir/uFScW38ba5RkS3BPPQAAr0RQgfPlH5cW9pbKi+tvF2iVJm0nrAAA6kRQgfMV51aFlOHvSy071d7mzEFpZUpVW4IKAKAOBBW4TstOUlyip6sAAHgxBtMCAADTIqgAJpOZU6gT+SWeLgMATMHpQeXFF1+UxWKptnTp0sW+/cKFC5o0aZKioqLUvHlzjRgxQqdPn3Z2GYDXaREapJBAf01ekaGBr28irACAXNSjctNNN+nUqVP2ZcuWLfZtU6ZM0V//+ld9/PHH2rRpk06ePKnhw4e7ogzAq8TbQrT2qb6aPzJRJeUVyisq83RJAOBxLhlMGxAQoJiYmBrrz507pw8++EDLli3T3XffLUlatGiRunbtqq1bt+r22293RTmA14i3hSgvurmnywAA03BJj8qhQ4cUFxendu3aafTo0Tp27JgkadeuXSovL9fAgQPtbbt06aLWrVsrPT29zv2VlpaqoKCg2gIAAHyf04NKUlKSFi9erDVr1ujdd99VVlaW7rzzTp0/f17Z2dkKCgqSzWar9p5WrVopOzu7zn3OnTtXERER9iUhgXk3AABoCpx+6WfIkCH2n7t3766kpCTdcMMN+uijjxQSEtKofU6fPl1paWn21wUFBYQVAACaAJffnmyz2dSpUydlZmYqJiZGZWVlys/Pr9bm9OnTtY5puSQ4OFjh4eHVFgAA4PtcHlQKCwt1+PBhxcbGqlevXgoMDNS6devs2w8cOKBjx44pOTnZ1aUAAAAv4/RLP1OnTtWwYcN0ww036OTJk5o1a5b8/f01atQoRUREaNy4cUpLS1NkZKTCw8P1+OOPKzk5mTt+AABADU4PKt9//71GjRql3NxcXXfddbrjjju0detWXXfddZKk3/72t/Lz89OIESNUWlqqQYMG6Z133nF2GQAAwAc4PagsX7683u3NmjXTwoULtXDhQmcfGgAA+Bie9QMAAEyLoAIAAEzLJVPoAw125mDjtnnYifwS5RWVqUVokOJtjZsfyG3yj0vFufW3sUZJNuYmAmA+BBV4hjVKCrRKK1PqbxdorWprIifySzTw9U0qKa9QSKC/1j7V17xhJf+4tLC3VF5cf7tAqzRpO2EFgOkQVOAZtoSqL0Yv/Jd+XlGZSsor9PjdHbRgfabyisrsQeVEfokycwo9XOFlinOrQsrw96WWnWpvc+ZgVWAszjXduQYAggo8x5bg1V+MV/aiXNnT0iI0yEOV1aJlJyku0dNVAIDDCCrAFRo7/uRST8v8kYm6rW2keS8HAYAXIagAl3HG+JMO0c0JKQDgJNyeDKgqoOw5cU47ss7ax5+UlFcor6jM06UBQJNGjwqavMt7USQpJNBfN8dHeLgqAIBEUAGqjS3pEN1cLUKDHO5JMd3dPgDgIwgqwH90iG5u70lxJKicyC/Rz99LN+fdPgDg5QgqwDXibh8AcB0G0wJOwt0+AOB8BBUAAGBaBBUAAGBaBBWgHpk5hTqRX+LpMgCgySKoALVoERqkkEB/TV6RoYGvbyKsAICHcNcPUIt4W4jWPtVXO7LOavKKDO3IOiuZ9Y6eMwcbtw0AvABBBahDvC1Eahtp71m59Owf07BGSYFWaWVK/e0CrVVtAcALEVSAelzZs2KqZ//YEqRJ26Xi3PrbWaOq2l7N1XpfGrofAHAiggpwFfG2EOVFN/d0GbWzJVx7eHCkZ2bSdsIKALciqADXyOsH2jakZ+bMwaogU5xLUAHgVgQVoJEu3Rm0YH2m9z/jxxk9MwDgAgQVoJEujV/JKypTi9Agp98RlJlT6JL9AoA3IagA1yDeFuL0IHH5HC6X7jQirABoqpjwDXBAZk6hMnMKXXqMSz0180cmqqS8Qjuyznr/OBgAaCR6VIAGuLyXQ5LLx6TUNYcLPSsAmhqCCtAAl49HkeSWsSO1zeFCUAHQ1BBUgAZyxXiUhhzTtHO4AIAbMEYFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFkEFAACYFhO+AV7i0jOGeKIygKbE6T0qc+fO1W233aawsDBFR0fr/vvv14EDB6q16devnywWS7Vl4sSJzi4F8AmXP2fovgVbNPD1TTykEECT4fQelU2bNmnSpEm67bbbdPHiRT333HP60Y9+pH379ik0NNTeLiUlRXPmzLG/tlqtzi4F8AmXP2coM6eQ5/4AaFKcHlTWrFlT7fXixYsVHR2tXbt26a677rKvt1qtiomJcfbhAZ/kiecMAYAZuHyMyrlz5yRJkZGR1dYvXbpUf/rTnxQTE6Nhw4Zp5syZdfaqlJaWqrS01P66oKDAdQXj6vKPS8W5dW8/c9B9tTjoRH6J/QnIUtVlFQCAebk0qFRWVmry5Mnq06ePbr75Zvv6hx56SDfccIPi4uK0e/duTZs2TQcOHNDKlStr3c/cuXM1e/ZsV5aKhso/Li3sLZUX198u0CpZo9xTUwOdyC/RwNc3qaS8wr4uJNBf8x9M9FxRAIB6uTSoTJo0SXv27NGWLVuqrZ8wYYL9527duik2NlYDBgzQ4cOH1b59+xr7mT59utLS0uyvCwoKlJCQ4LrCUbfi3KqQMvx9qWWnuttZoySbuf6M8orKVFJeofkjE9Uhunm18R4AAHNyWVBJTU3V6tWrtXnzZl1//fX1tk1KSpIkZWZm1hpUgoODFRwc7JI60UgtO0lxiZ6uolE6RDfXzfERni4DANAATr892TAMpaam6pNPPtH69evVtm3bq74nIyNDkhQbG+vscgCflJlTyC3KAJoEp/eoTJo0ScuWLdNf/vIXhYWFKTs7W5IUERGhkJAQHT58WMuWLdPQoUMVFRWl3bt3a8qUKbrrrrvUvXt3Z5cD+JTL51QJCfTX2qf6cjcQAJ/m9B6Vd999V+fOnVO/fv0UGxtrX1asWCFJCgoK0tq1a/WjH/1IXbp00VNPPaURI0bor3/9q7NLAXzOpTlV5o9MVEl5BeNrAPg8p/eoGIZR7/aEhARt2rTJ2YcFmox4W4jyopt7ugwAcAseSggAAEyLoAIAAEyLoAIAAEyLoAIAAEzL5c/6AdCEXO05UJIpZy0GYF4EFQDO4chzoCZtJ6wAaBCCijfgX6kuxQyvTtKQ50CdOSitTKlqW9/fV3f+nef3CzA1gorZ8a9Ul7k0y+uC9ZkKCfRXi9AgT5fkG671OVDu/DvP7xdgegQVs3Pmv1JRzaVZXvOKytQiNIip6M3CnX/n+f0CTI+g4i28+GnFZhZvCyGgmJU7/87z+wWYFkEF8GKZOYXu7Q06c7Bx2wCgkQgqgBdy+1OUrVFV4zRWptTfLtBa1RYAnISgAnihS+NrdmSd1eQVGcorKnNtULElVA0m5e4YAG5GUAG8lNufomxLIIQAcDuCSlPSkPkiroZxCF7hRH5J07ib6Wp/H93Zw8N8LIBLEFSaiobOF9EQjEMwtRP5JRr4+iaVlFe4Z/yKJzgyZsYd858wHwvgMgSVpqIh80U0FP8qNLW8ojKVlFfo8bs7aMH6TNePX/GEhoyZcef8J8zHArgMQaWpYb6IJsPnwsmVzDhmht8vwOkIKp52tevajAmBL2I+FgANRFDxJEeuazMmBL6A+VgAOIig4kkNHTfCmJBrcukOmMycQk+X4jL1zVDr9tlr6+PN87HQC1Q37niCCxFUzIDr2i5z+R0wknzuKcm1zVBb3zbThBVv+sKiF6h+3PEEFyOowKs4Oj/IpTtg5o9MVIfo5ubpWXCS2maorW+bL312t3F2L5C3zf1yNWcO+u4dT/QUmQJBBV7D0flBTuSX2C/3dIhurpvjI9xVqlvVN0Ot22evNSNnXLJxRi+Qu+d+udqXbPEZacXDzptbqXWy62uW3BcM6CkyDYIKvIYj84NcGWp86XIPGshsl2zcOfeLI1+yv/g/ydqy8ceSnBMezBYMmBvHNAgq8AqX9444esnntraRTeaShy8PGHaYGQfuumt8jlkH6l+td8uMwYAxhB5HUIHpXUvvSIfo5k0ipFw+cFbyvUHDjeZtA3edzSxfso70bjnjEhJ8CkEFpndl78jlA0ZR5dLA2UvnprZBw6a6TRn1M9OAW2dwd++Wmca64JoRVOA1LvWOEFRqF28LqTWEmPY2ZdRktoctOpO7erfMNtYF14ygAvg4blP2ImZ72KI3YhCszyGoAE0Atyl7kaY+rsZZzDI+B9eMoOJLvGSK76tN2nZpu6RGDQi9/A4hwGd5ye87cK0IKr7AbPNF1ONqk7bVNuX9/AcTG71/7nyBz/Gi33fAGQgqvsCM80XU4WqTtl1+h4+kGtPCX662XpPMnMImOX+KI648b9wJ5GW86PfdZehNalIIKr7Cy65rX+2LsUM94ymunDPkSiGB/oSUWtR13rgTyAt52e+709Cb1CQRVOBSjj5EsCGunDPkSvQQ1K6285aZU6jJKzK0I+us9J9w54o/M8ApzNqb5Gvz3pgMQQUuU9t4FGepa84Q1O/K83blHCv/O663Hvlge4Mf/Ai4nZl6k3x53hsTIag0lrMej+7DahuPUp+67tY5kV/iqhKbvCvnWDn8nzE+DXnwI+D1rnWsi7vnvWno946P9eB4NKgsXLhQr776qrKzs9WjRw8tWLBAvXv39mRJDdPQmQ8boglcS23IF11dd+uEBPprwfpM7uBxodrmWCGcwKc5c6yL2WbclXyuB8djQWXFihVKS0vTe++9p6SkJM2fP1+DBg3SgQMHFB0d7amyqlwttTbkKZ8N5WPJtz513aXTIjSozqcdXxpTwXgJAE7jibEu1zqOpaFPxG5oD44XPQ/JY0HljTfeUEpKin75y19Kkt577z199tln+sMf/qBnn33WU2U59pwInvLZILU92bd9dPNqYyMuzZVy5dOOGYsCwCXc1RPi7HEszphx18ueh+SRoFJWVqZdu3Zp+vTp9nV+fn4aOHCg0tPTa7QvLS1VaWmp/fW5c+ckSQUFBc4v7vR3UmGRNOwtKapD3e1CIiW/CMkVNfiIwvMFqiwtVoR/uT5JuUX5xVVjVGzWIMXZAvRJyi36+rs8PbvyW+08eEKVpcUqPF+gggKLhytvei79WR0+8YMqS4tVXHiePw/AGfwipIfXSSVn626Tmyn99Qlp37q6v3dyM6VSQzpfWP/3zvnCqnZHM6p+rmtfV/ueu1TT6e+qPoOTXPreNgyj4W8yPODEiROGJOOrr76qtv7pp582evfuXaP9rFmzDEksLCwsLCwsPrAcP368wZnBK+76mT59utLS0uyvKysrdfbsWUVFRcliafi/9goKCpSQkKDjx48rPDzcFaX6LM5d43HuGofz1nicu8bj3DVeQ86dYRg6f/684uLiGrxfjwSVli1byt/fX6dPn662/vTp04qJianRPjg4WMHBwdXW2Wy2Rh8/PDycv4CNxLlrPM5d43DeGo9z13icu8a72rmLiIhwaH9+11pQYwQFBalXr15at26dfV1lZaXWrVun5ORkT5QEAABMyGOXftLS0jRmzBjdeuut6t27t+bPn6+ioiL7XUAAAAAeCyojR47UDz/8oBdeeEHZ2dlKTEzUmjVr1KpVK5cdMzg4WLNmzapxGQlXx7lrPM5d43DeGo9z13icu8Zz1bmzGIYj9wgBAAC4j0fGqAAAADQEQQUAAJgWQQUAAJgWQQUAAJiWzwWVhQsXqk2bNmrWrJmSkpK0ffv2etvn5+dr0qRJio2NVXBwsDp16qTPP//cTdWai6Pnbv78+ercubNCQkKUkJCgKVOm6MKFC26q1hw2b96sYcOGKS4uThaLRatWrbrqezZu3KiePXsqODhYHTp00OLFi11epxk5eu5Wrlype+65R9ddd53Cw8OVnJysv//97+4p1mQa8/fukn/+858KCAhQYmKiy+ozs8acu9LSUs2YMUM33HCDgoOD1aZNG/3hD39wfbEm0pjztnTpUvXo0UNWq1WxsbF69NFHlZt7lSc218KngsqKFSuUlpamWbNm6euvv1aPHj00aNAg5eTk1Nq+rKxM99xzj44ePao///nPOnDggN5//33Fx8e7uXLPc/TcLVu2TM8++6xmzZql/fv364MPPtCKFSv03HPPublyzyoqKlKPHj20cOHCBrXPysrSvffeq/79+ysjI0OTJ0/W+PHjm+QXrqPnbvPmzbrnnnv0+eefa9euXerfv7+GDRumb775xsWVmo+j5+6S/Px8PfLIIxowYICLKjO/xpy7Bx54QOvWrdMHH3ygAwcO6MMPP1Tnzp1dWKX5OHre/vnPf+qRRx7RuHHjtHfvXn388cfavn27UlKu8hTp2lzb4wXNpXfv3sakSZPsrysqKoy4uDhj7ty5tbZ/9913jXbt2hllZWXuKtG0HD13kyZNMu6+++5q69LS0ow+ffq4tE4zk2R88skn9bZ55plnjJtuuqnaupEjRxqDBg1yYWXm15BzV5sbb7zRmD17tvML8iKOnLuRI0cazz//vDFr1iyjR48eLq3LGzTk3P3tb38zIiIijNzcXPcU5QUact5effVVo127dtXWvfXWW0Z8fLzDx/OZHpWysjLt2rVLAwcOtK/z8/PTwIEDlZ6eXut7Pv30UyUnJ2vSpElq1aqVbr75Zr3yyiuqqKhwV9mm0Jhz9//+3//Trl277JeHjhw5os8//1xDhw51S83eKj09vdp5lqRBgwbVeZ5Rt8rKSp0/f16RkZGeLsUrLFq0SEeOHNGsWbM8XYpX+fTTT3XrrbfqN7/5jeLj49WpUydNnTpVJSUlni7N1JKTk3X8+HF9/vnnMgxDp0+f1p///OdGfUd4xdOTG+LMmTOqqKioMbNtq1at9O9//7vW9xw5ckTr16/X6NGj9fnnnyszM1O/+tWvVF5e3qR+mRtz7h566CGdOXNGd9xxhwzD0MWLFzVx4sQmd+nHUdnZ2bWe54KCApWUlCgkJMRDlXmf1157TYWFhXrggQc8XYrpHTp0SM8++6y+/PJLBQT4zP/23eLIkSPasmWLmjVrpk8++URnzpzRr371K+Xm5mrRokWeLs+0+vTpo6VLl2rkyJG6cOGCLl68qGHDhjl8uVLysTEqjqqsrFR0dLR+97vfqVevXho5cqRmzJih9957z9Olmd7GjRv1yiuv6J133tHXX3+tlStX6rPPPtNLL73k6dLQBCxbtkyzZ8/WRx99pOjoaE+XY2oVFRV66KGHNHv2bHXq1MnT5XidyspKWSwWLV26VL1799bQoUP1xhtvaMmSJfSq1GPfvn168skn9cILL2jXrl1as2aNjh49qokTJzq8L5+J1i1btpS/v79Onz5dbf3p06cVExNT63tiY2MVGBgof39/+7quXbsqOztbZWVlCgoKcmnNZtGYczdz5kw9/PDDGj9+vCSpW7duKioq0oQJEzRjxgz5+TXpDFynmJiYWs9zeHg4vSkNtHz5co0fP14ff/xxjctoqOn8+fPauXOnvvnmG6Wmpkqq+vI1DEMBAQH6xz/+obvvvtvDVZpXbGys4uPjFRERYV/XtWtXGYah77//Xh07dvRgdeY1d+5c9enTR08//bQkqXv37goNDdWdd96pl19+WbGxsQ3el898mwQFBalXr15at26dfV1lZaXWrVun5OTkWt/Tp08fZWZmqrKy0r7u4MGDio2NbTIhRWrcuSsuLq4RRi4FPoPHR9UpOTm52nmWpC+++KLO84zqPvzwQ/3yl7/Uhx9+qHvvvdfT5XiF8PBwffvtt8rIyLAvEydOVOfOnZWRkaGkpCRPl2hqffr00cmTJ1VYWGhfd/DgQfn5+en666/3YGXm5tTvCIeH35rY8uXLjeDgYGPx4sXGvn37jAkTJhg2m83Izs42DMMwHn74YePZZ5+1tz927JgRFhZmpKamGgcOHDBWr15tREdHGy+//LKnPoLHOHruZs2aZYSFhRkffvihceTIEeMf//iH0b59e+OBBx7w1EfwiPPnzxvffPON8c033xiSjDfeeMP45ptvjO+++84wDMN49tlnjYcfftje/siRI4bVajWefvppY//+/cbChQsNf39/Y82aNZ76CB7j6LlbunSpERAQYCxcuNA4deqUfcnPz/fUR/AYR8/dlZryXT+Onrvz588b119/vfGzn/3M2Lt3r7Fp0yajY8eOxvjx4z31ETzC0fO2aNEiIyAgwHjnnXeMw4cPG1u2bDFuvfVWo3fv3g4f26eCimEYxoIFC4zWrVsbQUFBRu/evY2tW7fat/Xt29cYM2ZMtfZfffWVkZSUZAQHBxvt2rUzfv3rXxsXL150c9Xm4Mi5Ky8vN1588UWjffv2RrNmzYyEhATjV7/6lZGXl+f+wj1ow4YNhqQay6VzNWbMGKNv37413pOYmGgEBQUZ7dq1MxYtWuT2us3A0XPXt2/fets3JY35e3e5phxUGnPu9u/fbwwcONAICQkxrr/+eiMtLc0oLi52f/Ee1Jjz9tZbbxk33nijERISYsTGxhqjR482vv/+e4ePbTEM+ukBAIA5+cwYFQAA4HsIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLQIKgAAwLT+PzjR7Pu6yKWsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.0029 1.00033 and their SDs are 0.14557 0.03874\n",
      "And here is the pop distro in state AZ\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 1538\n",
      "working on HD 500 out of 1538\n",
      "working on HD 1000 out of 1538\n",
      "working on HD 1500 out of 1538\n",
      "all done checking HD and complement contiguity for all 1538 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro in state\", STATE)\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f90621-e7fc-4891-a5cd-69033c3c48e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy one of two above blocks and run again if needed (n/a for NC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.06\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.94\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for WA, above is first run-through after overuse, then underuse pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"PatchedUnits.csv\" #\"contigUnpatchedB.csv\"  #fullyPatched #overOnlypatched\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confirming vtd 0 is not in the map but missing from the parent vtd list\n"
     ]
    }
   ],
   "source": [
    "for v in range(nVTDs):\n",
    "    if v %2000 == 0:\n",
    "        print(\"confirming vtd\",v,\"is not in the map but missing from the parent vtd list\")\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(\"Uhoh,\",v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"RedonepatchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7434 7434 7434 7434 7434 7434\n"
     ]
    }
   ],
   "source": [
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 1538\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f5a9e618-3a09-42fd-93eb-3b91a88bba4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if STATE == \"WA\":\n",
    "    allCutLists = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "10 0 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patched0p07352WithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that VTDs are represented 1.0 across all units\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"checking that VTDs are represented 1.0 across all units\")\n",
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "plotVTDs, plotUses = list(), list()\n",
    "for v in range(nVTDs):\n",
    "    if tractPop[v] > 0  and v not in allCutVTDs:\n",
    "        plotVTDs.append(v)\n",
    "        plotUses.append(vtdUSE[v])\n",
    "plt.scatter(plotVTDs, plotUses)\n",
    "plt.show()\n",
    "for i,v in enumerate(plotVTDs):\n",
    "    if plotUses[i] > 0.1 and plotUses[i] < 0.9 :\n",
    "        print(v,tractPop[v], plotUses[i],\"vtd no, its pop, and usage across all units\")\n",
    "        plotPoly(tractGeom[v])\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops.  These should all be within single digits\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGdCAYAAAAfTAk2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxI0lEQVR4nO3de3xU9Z3/8fckkAnRXIwkJFkjF9kFIig3icFuQUkhhap0WS1uFLEUKiUqF5Xwe1QQEYNFaYvyK9oVwYqLqy1WUaMpCLQSAYOpICFVFwhIQtSUGQIScjm/P/wx2yG3mWSuX17Px+M8dM58zpnP16/DvDlzzhmbZVmWAAAADBIR7AYAAAB8jYADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADBOl2A3EAxNTU06duyYYmNjZbPZgt0OAADwgGVZOnnypNLS0hQR0fYxmgsy4Bw7dkzp6enBbgMAAHTAkSNHdNlll7VZc0EGnNjYWEnf/geKi4sLcjcAAMATTqdT6enprs/xtlyQAefc11JxcXEEHAAAwownp5dwkjEAADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYJwL8kZ//nK2oUm/Kz6kwzWn1TMxRndk9VJUFzJkuGhssrTrYI2qT55Rcmy0RvROVGQEv1UWThyn6/Xjtbt0zHFGafHRWjN1hOJjuga7LXjhS2edfvh//6KaU/VKvKirNv7sO0qKswe7LYQhm2VZVrCbCDSn06n4+Hg5HA6f3cm44K39enb7Qf3jf0ybpBnf7a0F4zN88hrwn8J9lVr8xn5VOs641qXGR2vRjRnKGZgaxM7gqVHLt+jw1980W9/z0m7a9sANQegI3rrq4XfkPNPQbH1cdBd9/PC4IHSEUOPN5zeHF3yg4K39eua8cCNJlqRnth9UwVv7g9EWPFS4r1IzX9zjFm4kqcpxRjNf3KPCfZVB6gyeai3cSNLhr7/RqOVbAtwRvNVauJEk55kGXfXwOwHuCOGOgNNJZxua9Oz2g23WPLv9oM42NAWoI3ijscnS4jf2NwunklzrFr+xX41NF9yBzrDhOF3farg55/DX38hxuj5AHcFbXzrrWg035zjPNOhLZ12AOkJn1NSe1dgVWzV48bsau2KramrPBqUPAk4nrdvR/MjN+az/X4fQs+tgTbMjN//IklTpOKNdB2sC1xS88uO1u3xah8CbuOovPq1D8FzzaJGGPlqkv1Wf0olv6vW36lMa+miRrnm0KOC9EHA6afehv/u0DoFVfbL1cNOROgTesTYCakfqEHhfnfLsyIyndQiOax4t0petHK35svZswEMOAaeTYqIifVqHwEqOjfZpHQIvNd6zufG0DoFnj/TsakVP6xB4NbVnWw0353xZezagX1cRcDopI8Wzq7A8rUNgjeidqNT4aLX2x6ZN334wjuidGMi24IVZ/3qFT+sQeKfqPDtH0dM6BN7kZ3f4tM4XCDid5On9GbiPQ2iKjLBp0Y0ZrZ5HZUladGMG98MJYbWNjT6tQ+B5GluIN6Gr+qRnR2Y8rfMFAk4npcR382kdAq/g7bJOPY/g4mvG8Netq2cfRZ7WIfCSY6N8WucLAfm/ZdWqVerVq5eio6OVmZmpXbvavprhlVdeUf/+/RUdHa1BgwbprbfecnvesiwtXLhQqamp6tatm7Kzs/Xpp5/6cwit6tcj1qd1CCwuMQ5/w3peovYOsEXYvq1DaHrznu/6tA6Bt/4nWT6t8wW/B5yXX35Zc+fO1aJFi7Rnzx5dffXVGjdunKqrq1us37Fjh2677TZNmzZNH330kSZOnKiJEydq3759rppf/OIXWrlypVavXq2dO3fqoosu0rhx43TmTOCvkpj+wm6f1iGw7np+p0/rEHglh/+u9m5T1GR9W4fQVOXhVYqe1iHwPvuy1qd1vuD3gLNixQpNnz5dd911lzIyMrR69WrFxMRozZo1Ldb/+te/Vk5Ojh544AENGDBAS5Ys0dChQ/X0009L+vboza9+9Sv9/Oc/180336yrrrpKL7zwgo4dO6bXXnvN38NppqTihE/rEFh7jjh8WofAm/XbD3xah8C7zcO58bQOgbf186M+rfMFvwacs2fPqqSkRNnZ2f/7ghERys7OVnFxcYvbFBcXu9VL0rhx41z1Bw8eVFVVlVtNfHy8MjMzW91nXV2dnE6n2wLADJ7egpFbNQL+88wWz4KLp3W+4NeA89VXX6mxsVE9evRwW9+jRw9VVVW1uE1VVVWb9ef+6c0+CwoKFB8f71rS09M7NB4AABAeLohT0hcsWCCHw+Fajhw5EuyWAACAH/k14HTv3l2RkZE6fvy42/rjx48rJSWlxW1SUlLarD/3T2/2abfbFRcX57YAAADfiPbwVmGe1vmCXwNOVFSUhg0bps2bN7vWNTU1afPmzcrKavlSsaysLLd6SSoqKnLV9+7dWykpKW41TqdTO3fubHWf/tTDwxv4eVqHwPqv6df6tA6BN+O7vXxah8CbNdrDu1F7WIfAi+zq2c8ReVrnC37/imru3Ln67W9/q3Xr1qmsrEwzZ87UqVOndNddd0mSpkyZogULFrjq77vvPhUWFurJJ5/UgQMH9PDDD+vDDz9UXl6eJMlms2n27Nl69NFH9frrr2vv3r2aMmWK0tLSNHHiRH8Pp5mHxw3waR0C69xPNbSFn2oIbaP+uUf7RV7UIfBG9u3u0zoEXlqCZzfS9LTOF/wecH70ox/piSee0MKFCzV48GCVlpaqsLDQdZJwRUWFKisrXfUjR47USy+9pGeffVZXX321Xn31Vb322msaOHCgq+bBBx/UPffcoxkzZuiaa65RbW2tCgsLFR0d+DuVHvXw1209rUNgnfuphrZ+i4qfaght115xqRJiurZZkxDTVddecWmAOoK3ru3T/hxeEtNV1/ZhDkPVyzNG+rTOF2yWZbVziyzzOJ1OxcfHy+FwdPp8nIV/3KcXig+3Wzclq6ceuXlgu3UIjsJ9lVr8xn5VOv73RmKp8dFadGOGcgamBrEzeKJwX6XufnFPq8+vvn0o8xjimMPwd82jRW3+onjSxVHa/fPvdeo1vPn87tKpV4ISotv+W4e3dQiOnIGp+l5GinYdrFH1yTNKjv32aymO3ISHnIGpWn37UD38+ieqcv7v0dKUOLsevulKPhjDwP/O4X5VOfmLRjja/fPvtRpyfBFuvMURnE4ewRm08C2dPNv+f8LYKJv2PjK+U68FoG2NTRYhNcwxh+GvpvasJj+7Q9Unzyo5NkobZoxU4sW++ZFNjuAE0Ol6z/Khp3UAOi4ywqYszrUJa8xh+Eu8OErvzh0d7DYujBv9+dNFds8uefO0DgAAdB4Bp5PevneUT+sAAEDnEXA66Z8Suykqsu3vh6MibfqnxG4B6ggAABBwfOBvS8e3GnKiIm3621JOLgYAIJA4ydhH/rZ0vL6o+UbfX7lNp+oadZE9Um/fO4ojNwAABAEBx4f+KbGbPn44J9htAABwweMrKgAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBy/Bpyamhrl5uYqLi5OCQkJmjZtmmpra9usv+eee9SvXz9169ZNl19+ue699145HA63OpvN1mzZsGGDP4cCAADCSBd/7jw3N1eVlZUqKipSfX297rrrLs2YMUMvvfRSi/XHjh3TsWPH9MQTTygjI0OHDx/W3XffrWPHjunVV191q33++eeVk5PjepyQkODPoQAAAqCxydKugzWqPnlGybHRGtE7UZERtmC3hTBksyzL8seOy8rKlJGRod27d2v48OGSpMLCQo0fP15Hjx5VWlqaR/t55ZVXdPvtt+vUqVPq0uXbPGaz2bRx40ZNnDixQ705nU7Fx8fL4XAoLi6uQ/sAAPhW4b5KLX5jvyodZ1zrUuOjtejGDOUMTA1iZwgV3nx+++0rquLiYiUkJLjCjSRlZ2crIiJCO3fu9Hg/5wZxLtycM2vWLHXv3l0jRozQmjVr5KecBgAIgMJ9lZr54h63cCNJVY4zmvniHhXuqwxSZwhXfvuKqqqqSsnJye4v1qWLEhMTVVVV5dE+vvrqKy1ZskQzZsxwW//II4/ohhtuUExMjN5991397Gc/U21tre69994W91NXV6e6ujrXY6fT6eVoAAD+0thkafEb+9XSX1PPrVv8xn59LyOFr6vgMa+P4OTn57d4ku8/LgcOHOh0Y06nUxMmTFBGRoYefvhht+ceeughXXfddRoyZIjmz5+vBx98UMuXL291XwUFBYqPj3ct6enpne4PAOAbuw7WNDtyc75KxxntOlgToI5gAq+P4MybN09Tp05ts6ZPnz5KSUlRdXW12/qGhgbV1NQoJSWlze1PnjypnJwcxcbGauPGjeratWub9ZmZmVqyZInq6upkt9ubPb9gwQLNnTvX9djpdBJyACBEVDnbDjfe1gFSBwJOUlKSkpKS2q3LysrSiRMnVFJSomHDhkmStmzZoqamJmVmZra6ndPp1Lhx42S32/X6668rOjq63dcqLS3VJZdc0mK4kSS73d7qcwCA4PrqZF37RV7UAZIfz8EZMGCAcnJyNH36dK1evVr19fXKy8vT5MmTXVdQffHFFxozZoxeeOEFjRgxQk6nU2PHjtXp06f14osvyul0us6XSUpKUmRkpN544w0dP35c1157raKjo1VUVKTHHntM999/v7+G4rGzDU36XfEhHa45rZ6JMbojq5eiunAvxXCx/6hTP3j6z2rSt9/dbsr7V2VcxlV24YRLjMPT3097Flw8rUNwhcr70K/3wVm/fr3y8vI0ZswYRUREaNKkSVq5cqXr+fr6epWXl+v06dOSpD179riusOrbt6/bvg4ePKhevXqpa9euWrVqlebMmSPLstS3b1+tWLFC06dP9+dQ2lXw1n799s8H1fQPZ8ktfatM0/+1txaMzwheY/BIr/w33R43SRr/9J8lSYeWTQhCR/AWlxiHrwibZ38R9LQOwRNK70O/3QcnlPn6PjgFb+3XM9sPtvr8T79LyAll54eblhByQtu5S4xb+sPMJuk3tw8l5ISw9z/7Srn/2f7tQ9b/JFPX9e0egI7QEYF4H4bEfXAuFGcbmvTbP7cebiTpt38+qLMNTQHqCN7Yf9SzWwZ4WofAa+sSY+nby4wXv7FfjU0X3N/lwsY1vRJla+cbDJvt2zqEplB8HxJwOul3xYfU3nw1Wd/WIfT84P9/DeWrOgQelxiHv5LDf1d73yVY1rd1CE2h+D4k4HTSkjfLfFqHwPL0uBrH30LX4a9P+bQOgfenTzy7S7GndQi8koqvfFrnCwQcAGFtzV/+x6d1CLzndhz2aR0C74l3PvNpnS8QcACENeeZBp/WATADAQdAWPunhG4+rQNgBgIOLmjjB0b5tA6Bt2bqCJ/WIfDuGuXZpcOe1iHw5o3r7dM6XyDgdFK3Lp7dndHTOgTWuIGe3Z/I0zoEXnxMV/W8tO2jMz0v7ab4mLZ/0w7BM/Zfevq0DoE3/PIePq3zBQJOJ70ze7RP6xBYybHt/9aZN3UIjm0P3NBqyOl5aTdte+CGAHcEb4zonajU+LbfY6nx397yH6EpFOeQgNNJl3ePUXs/N9Ul4ts6hJ5zb8rWjq/ZxB+s4WLbAzforwvHatjlCUqNj9awyxP014VjCTdhIDLCpkU3ZrT5Plx0Ywa/KxbCQnEO+akGH/xUgyT1/T9vqqWbFXeJkD57jNv8h7JztxeX5HYXznNvQ27zDwRGKP2OETrG33Pozec3AcdHAUeSKr46rZxfb9M39U3q1jVChfeN4shNmOAPViA0hMovUaPj/DmHBJx2+CvgILzxBysAhDZvPr+7BKgnIORFRtiUdcWlwW4DAOADnGQMAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOH4NODU1NcrNzVVcXJwSEhI0bdo01dbWtrnN6NGjZbPZ3Ja7777braaiokITJkxQTEyMkpOT9cADD6ihocGfQwEAAGGkiz93npubq8rKShUVFam+vl533XWXZsyYoZdeeqnN7aZPn65HHnnE9TgmJsb1742NjZowYYJSUlK0Y8cOVVZWasqUKeratasee+wxv40FAACED5tlWZY/dlxWVqaMjAzt3r1bw4cPlyQVFhZq/PjxOnr0qNLS0lrcbvTo0Ro8eLB+9atftfj822+/rR/84Ac6duyYevToIUlavXq15s+fry+//FJRUVHt9uZ0OhUfHy+Hw6G4uLiODRAAAASUN5/ffvuKqri4WAkJCa5wI0nZ2dmKiIjQzp0729x2/fr16t69uwYOHKgFCxbo9OnTbvsdNGiQK9xI0rhx4+R0OvXJJ5+0uL+6ujo5nU63BQAAmMtvX1FVVVUpOTnZ/cW6dFFiYqKqqqpa3e4//uM/1LNnT6Wlpenjjz/W/PnzVV5erj/84Q+u/f5juJHketzafgsKCrR48eLODAcAAIQRrwNOfn6+Hn/88TZrysrKOtzQjBkzXP8+aNAgpaamasyYMfr88891xRVXdGifCxYs0Ny5c12PnU6n0tPTO9wjAAAIbV4HnHnz5mnq1Klt1vTp00cpKSmqrq52W9/Q0KCamhqlpKR4/HqZmZmSpM8++0xXXHGFUlJStGvXLrea48ePS1Kr+7Xb7bLb7R6/JgAACG9eB5ykpCQlJSW1W5eVlaUTJ06opKREw4YNkyRt2bJFTU1NrtDiidLSUklSamqqa79Lly5VdXW16yuwoqIixcXFKSMjw8vRAAAAE/ntJOMBAwYoJydH06dP165du/T+++8rLy9PkydPdl1B9cUXX6h///6uIzKff/65lixZopKSEh06dEivv/66pkyZou9+97u66qqrJEljx45VRkaG7rjjDv31r3/VO++8o5///OeaNWsWR2kAAIAkP9/ob/369erfv7/GjBmj8ePH6zvf+Y6effZZ1/P19fUqLy93XSUVFRWlP/3pTxo7dqz69++vefPmadKkSXrjjTdc20RGRmrTpk2KjIxUVlaWbr/9dk2ZMsXtvjkAAODC5rf74IQy7oMDAED4CYn74AAAAAQLAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGMevAaempka5ubmKi4tTQkKCpk2bptra2lbrDx06JJvN1uLyyiuvuOpaen7Dhg3+HAoAAAgjXfy589zcXFVWVqqoqEj19fW66667NGPGDL300kst1qenp6uystJt3bPPPqvly5fr+9//vtv6559/Xjk5Oa7HCQkJPu8fAACEJ78FnLKyMhUWFmr37t0aPny4JOmpp57S+PHj9cQTTygtLa3ZNpGRkUpJSXFbt3HjRt166626+OKL3dYnJCQ0qwUAAJD8+BVVcXGxEhISXOFGkrKzsxUREaGdO3d6tI+SkhKVlpZq2rRpzZ6bNWuWunfvrhEjRmjNmjWyLKvV/dTV1cnpdLotAADAXH47glNVVaXk5GT3F+vSRYmJiaqqqvJoH88995wGDBigkSNHuq1/5JFHdMMNNygmJkbvvvuufvazn6m2tlb33ntvi/spKCjQ4sWLOzYQAAAQdrw+gpOfn9/qicDnlgMHDnS6sW+++UYvvfRSi0dvHnroIV133XUaMmSI5s+frwcffFDLly9vdV8LFiyQw+FwLUeOHOl0fwAAIHR5fQRn3rx5mjp1aps1ffr0UUpKiqqrq93WNzQ0qKamxqNzZ1599VWdPn1aU6ZMabc2MzNTS5YsUV1dnex2e7Pn7XZ7i+sBAICZvA44SUlJSkpKarcuKytLJ06cUElJiYYNGyZJ2rJli5qampSZmdnu9s8995xuuukmj16rtLRUl1xyCSEGAABI8uM5OAMGDFBOTo6mT5+u1atXq76+Xnl5eZo8ebLrCqovvvhCY8aM0QsvvKARI0a4tv3ss8+0fft2vfXWW832+8Ybb+j48eO69tprFR0draKiIj322GO6//77/TUUAAAQZvx6H5z169crLy9PY8aMUUREhCZNmqSVK1e6nq+vr1d5eblOnz7ttt2aNWt02WWXaezYsc322bVrV61atUpz5syRZVnq27evVqxYoenTp/tzKAAAIIzYrLaurzaU0+lUfHy8HA6H4uLigt0OAADwgDef3/wWFQAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACM47eAs3TpUo0cOVIxMTFKSEjwaBvLsrRw4UKlpqaqW7duys7O1qeffupWU1NTo9zcXMXFxSkhIUHTpk1TbW2tH0YAAADCld8CztmzZ3XLLbdo5syZHm/zi1/8QitXrtTq1au1c+dOXXTRRRo3bpzOnDnjqsnNzdUnn3yioqIibdq0Sdu3b9eMGTP8MQQAABCmbJZlWf58gbVr12r27Nk6ceJEm3WWZSktLU3z5s3T/fffL0lyOBzq0aOH1q5dq8mTJ6usrEwZGRnavXu3hg8fLkkqLCzU+PHjdfToUaWlpXnUk9PpVHx8vBwOh+Li4jo1PgAAEBjefH6HzDk4Bw8eVFVVlbKzs13r4uPjlZmZqeLiYklScXGxEhISXOFGkrKzsxUREaGdO3e2uu+6ujo5nU63BQAAmCtkAk5VVZUkqUePHm7re/To4XquqqpKycnJbs936dJFiYmJrpqWFBQUKD4+3rWkp6f7uHsAABBKvAo4+fn5stlsbS4HDhzwV68dtmDBAjkcDtdy5MiRYLcEAAD8qIs3xfPmzdPUqVPbrOnTp0+HGklJSZEkHT9+XKmpqa71x48f1+DBg1011dXVbts1NDSopqbGtX1L7Ha77HZ7h/oCAADhx6uAk5SUpKSkJL800rt3b6WkpGjz5s2uQON0OrVz507XlVhZWVk6ceKESkpKNGzYMEnSli1b1NTUpMzMTL/0BQAAwo/fzsGpqKhQaWmpKioq1NjYqNLSUpWWlrrds6Z///7auHGjJMlms2n27Nl69NFH9frrr2vv3r2aMmWK0tLSNHHiREnSgAEDlJOTo+nTp2vXrl16//33lZeXp8mTJ3t8BRUAADCfV0dwvLFw4UKtW7fO9XjIkCGSpPfee0+jR4+WJJWXl8vhcLhqHnzwQZ06dUozZszQiRMn9J3vfEeFhYWKjo521axfv155eXkaM2aMIiIiNGnSJK1cudJfwwAAAGHI7/fBCUXcBwcAgPATlvfBAQAA8BUCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwjt8CztKlSzVy5EjFxMQoISGh3fr6+nrNnz9fgwYN0kUXXaS0tDRNmTJFx44dc6vr1auXbDab27Js2TI/jQIAAIQjvwWcs2fP6pZbbtHMmTM9qj99+rT27Nmjhx56SHv27NEf/vAHlZeX66abbmpW+8gjj6iystK13HPPPb5uHwAAhLEu/trx4sWLJUlr1671qD4+Pl5FRUVu655++mmNGDFCFRUVuvzyy13rY2NjlZKS4rNeAQCAWUL6HByHwyGbzdbsK65ly5bp0ksv1ZAhQ7R8+XI1NDS0uZ+6ujo5nU63BQAAmMtvR3A668yZM5o/f75uu+02xcXFudbfe++9Gjp0qBITE7Vjxw4tWLBAlZWVWrFiRav7KigocB1RAgAA5rNZlmV5Wpyfn6/HH3+8zZqysjL179/f9Xjt2rWaPXu2Tpw44XFT9fX1mjRpko4ePaqtW7e6BZzzrVmzRj/96U9VW1sru93eYk1dXZ3q6upcj51Op9LT0+VwONrcNwAACB1Op1Px8fEefX57dQRn3rx5mjp1aps1ffr08WaXzdTX1+vWW2/V4cOHtWXLlnYHkJmZqYaGBh06dEj9+vVrscZut7cafgAAgHm8CjhJSUlKSkryVy+ucPPpp5/qvffe06WXXtruNqWlpYqIiFBycrLf+gIAAOHFb+fgVFRUqKamRhUVFWpsbFRpaakkqW/fvrr44oslSf3791dBQYF++MMfqr6+Xv/+7/+uPXv2aNOmTWpsbFRVVZUkKTExUVFRUSouLtbOnTt1/fXXKzY2VsXFxZozZ45uv/12XXLJJf4aCgAACDN+CzgLFy7UunXrXI+HDBkiSXrvvfc0evRoSVJ5ebkcDock6YsvvtDrr78uSRo8eLDbvs5tY7fbtWHDBj388MOqq6tT7969NWfOHM2dO9dfwwAAAGHIq5OMTeHNSUoAACA0ePP5HdL3wQEAAOgIAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGMdvAWfp0qUaOXKkYmJilJCQ4NE2U6dOlc1mc1tycnLcampqapSbm6u4uDglJCRo2rRpqq2t9cMIAABAuPJbwDl79qxuueUWzZw506vtcnJyVFlZ6Vr+67/+y+353NxcffLJJyoqKtKmTZu0fft2zZgxw5etAwCAMNfFXztevHixJGnt2rVebWe325WSktLic2VlZSosLNTu3bs1fPhwSdJTTz2l8ePH64knnlBaWlqnegYAAGYIuXNwtm7dquTkZPXr108zZ87U119/7XquuLhYCQkJrnAjSdnZ2YqIiNDOnTtb3WddXZ2cTqfbAgAAzBVSAScnJ0cvvPCCNm/erMcff1zbtm3T97//fTU2NkqSqqqqlJyc7LZNly5dlJiYqKqqqlb3W1BQoPj4eNeSnp7u13EAAIDg8irg5OfnNzsJ+PzlwIEDHW5m8uTJuummmzRo0CBNnDhRmzZt0u7du7V169YO71OSFixYIIfD4VqOHDnSqf0BAIDQ5tU5OPPmzdPUqVPbrOnTp09n+mm2r+7du+uzzz7TmDFjlJKSourqareahoYG1dTUtHrejvTteT12u91nfQEAgNDmVcBJSkpSUlKSv3pp5ujRo/r666+VmpoqScrKytKJEydUUlKiYcOGSZK2bNmipqYmZWZmBqwvAAAQ2vx2Dk5FRYVKS0tVUVGhxsZGlZaWqrS01O2eNf3799fGjRslSbW1tXrggQf0wQcf6NChQ9q8ebNuvvlm9e3bV+PGjZMkDRgwQDk5OZo+fbp27dql999/X3l5eZo8eTJXUAEAABe/XSa+cOFCrVu3zvV4yJAhkqT33ntPo0ePliSVl5fL4XBIkiIjI/Xxxx9r3bp1OnHihNLS0jR27FgtWbLE7eul9evXKy8vT2PGjFFERIQmTZqklStX+msYAAAgDNksy7KC3USgOZ1OxcfHy+FwKC4uLtjtAAAAD3jz+R1Sl4kDAAD4AgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABjHbwFn6dKlGjlypGJiYpSQkODRNjabrcVl+fLlrppevXo1e37ZsmV+GgUAAAhHXfy147Nnz+qWW25RVlaWnnvuOY+2qaysdHv89ttva9q0aZo0aZLb+kceeUTTp093PY6Nje18wwAAwBh+CziLFy+WJK1du9bjbVJSUtwe//GPf9T111+vPn36uK2PjY1tVgsAAHBOyJ6Dc/z4cb355puaNm1as+eWLVumSy+9VEOGDNHy5cvV0NDQ5r7q6urkdDrdFgAAYC6/HcHprHXr1ik2Nlb/9m//5rb+3nvv1dChQ5WYmKgdO3ZowYIFqqys1IoVK1rdV0FBgeuIEgAAMJ9XR3Dy8/NbPRH43HLgwAGfNLZmzRrl5uYqOjrabf3cuXM1evRoXXXVVbr77rv15JNP6qmnnlJdXV2r+1qwYIEcDodrOXLkiE96BAAAocmrIzjz5s3T1KlT26w5/3yZjvjzn/+s8vJyvfzyy+3WZmZmqqGhQYcOHVK/fv1arLHb7bLb7Z3uCwAAhAevAk5SUpKSkpL81YvLc889p2HDhunqq69ut7a0tFQRERFKTk72e18wW2OTpV0Ha1R98oySY6M1oneiIiNswW4LANABfjsHp6KiQjU1NaqoqFBjY6NKS0slSX379tXFF18sSerfv78KCgr0wx/+0LWd0+nUK6+8oieffLLZPouLi7Vz505df/31io2NVXFxsebMmaPbb79dl1xyib+G4rG8dUXaVHbW9fgHA6L09J3fC2JH8FThvkrd/eKeZutX3z5UOQNTg9ARAKAz/HYV1cKFCzVkyBAtWrRItbW1GjJkiIYMGaIPP/zQVVNeXi6Hw+G23YYNG2RZlm677bZm+7Tb7dqwYYNGjRqlK6+8UkuXLtWcOXP07LPP+msYHuuV/6ZbuJGkTWVn1Sv/zSB1BE+1Fm4k6e4X96hwX2WLzwEAQpfNsiwr2E0EmtPpVHx8vBwOh+Li4jq9P09CzKFlEzr9OvC9xiZLV/yft9qt+/yx8XxdBQBB5s3nd8jeBydc5K0r8mkdAmvKb9oPN97UAQBCAwGnk87/WqqzdQis9z28Y4CndQCA0EDAAQAAxiHgAAAA4xBwOukHA6J8WofAunlgN5/WAQBCAwGnkzy9zw33wwlNv779Bp/WAQBCAwHHB9q7BJxLxEMb8wcA5iHg+MihZROafQ31gwFRfDiGiUPLJjT7Gurmgd2YPwAIU9zozwc3+gMAAP7Hjf4AAMAFjYADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABinS7AbCIZzN292Op1B7gQAAHjq3Oe2Jz/CcEEGnJMnT0qS0tPTg9wJAADw1smTJxUfH99mzQX5W1RNTU06duyYYmNjZbPZfLpvp9Op9PR0HTlyxMjfuWJ84c/0MTK+8Gf6GE0fn+S/MVqWpZMnTyotLU0REW2fZXNBHsGJiIjQZZdd5tfXiIuLM/Z/XInxmcD0MTK+8Gf6GE0fn+SfMbZ35OYcTjIGAADGIeAAAADjEHB8zG63a9GiRbLb7cFuxS8YX/gzfYyML/yZPkbTxyeFxhgvyJOMAQCA2TiCAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4HbBq1Sr16tVL0dHRyszM1K5du9qsf+WVV9S/f39FR0dr0KBBeuuttwLUacd4M761a9fKZrO5LdHR0QHs1jvbt2/XjTfeqLS0NNlsNr322mvtbrN161YNHTpUdrtdffv21dq1a/3eZ0d5O76tW7c2mz+bzaaqqqrANOylgoICXXPNNYqNjVVycrImTpyo8vLydrcLp/dgR8YYTu/D3/zmN7rqqqtcN4DLysrS22+/3eY24TR/3o4vnOauJcuWLZPNZtPs2bPbrAvGHBJwvPTyyy9r7ty5WrRokfbs2aOrr75a48aNU3V1dYv1O3bs0G233aZp06bpo48+0sSJEzVx4kTt27cvwJ17xtvxSd/eqbKystK1HD58OIAde+fUqVO6+uqrtWrVKo/qDx48qAkTJuj6669XaWmpZs+erZ/85Cd65513/Nxpx3g7vnPKy8vd5jA5OdlPHXbOtm3bNGvWLH3wwQcqKipSfX29xo4dq1OnTrW6Tbi9BzsyRil83oeXXXaZli1bppKSEn344Ye64YYbdPPNN+uTTz5psT7c5s/b8UnhM3fn2717t5555hldddVVbdYFbQ4teGXEiBHWrFmzXI8bGxuttLQ0q6CgoMX6W2+91ZowYYLbuszMTOunP/2pX/vsKG/H9/zzz1vx8fEB6s63JFkbN25ss+bBBx+0rrzySrd1P/rRj6xx48b5sTPf8GR87733niXJ+vvf/x6QnnyturrakmRt27at1Zpwew+ez5MxhvP70LIs65JLLrH+8z//s8Xnwn3+LKvt8YXr3J08edL653/+Z6uoqMgaNWqUdd9997VaG6w55AiOF86ePauSkhJlZ2e71kVERCg7O1vFxcUtblNcXOxWL0njxo1rtT6YOjI+SaqtrVXPnj2Vnp7e7t9Uwk04zV9nDB48WKmpqfre976n999/P9jteMzhcEiSEhMTW60J9zn0ZIxSeL4PGxsbtWHDBp06dUpZWVkt1oTz/HkyPik8527WrFmaMGFCs7lpSbDmkIDjha+++kqNjY3q0aOH2/oePXq0es5CVVWVV/XB1JHx9evXT2vWrNEf//hHvfjii2pqatLIkSN19OjRQLTsd63Nn9Pp1DfffBOkrnwnNTVVq1ev1u9//3v9/ve/V3p6ukaPHq09e/YEu7V2NTU1afbs2bruuus0cODAVuvC6T14Pk/HGG7vw7179+riiy+W3W7X3XffrY0bNyojI6PF2nCcP2/GF25zJ0kbNmzQnj17VFBQ4FF9sObwgvw1cfhOVlaW299MRo4cqQEDBuiZZ57RkiVLgtgZPNGvXz/169fP9XjkyJH6/PPP9ctf/lK/+93vgthZ+2bNmqV9+/bpL3/5S7Bb8RtPxxhu78N+/fqptLRUDodDr776qu68805t27at1RAQbrwZX7jN3ZEjR3TfffepqKgo5E+GJuB4oXv37oqMjNTx48fd1h8/flwpKSktbpOSkuJVfTB1ZHzn69q1q4YMGaLPPvvMHy0GXGvzFxcXp27dugWpK/8aMWJEyIeGvLw8bdq0Sdu3b9dll13WZm04vQf/kTdjPF+ovw+joqLUt29fSdKwYcO0e/du/frXv9YzzzzTrDYc58+b8Z0v1OeupKRE1dXVGjp0qGtdY2Ojtm/frqefflp1dXWKjIx02yZYc8hXVF6IiorSsGHDtHnzZte6pqYmbd68udXvV7OystzqJamoqKjN72ODpSPjO19jY6P27t2r1NRUf7UZUOE0f75SWloasvNnWZby8vK0ceNGbdmyRb179253m3Cbw46M8Xzh9j5sampSXV1di8+F2/y1pK3xnS/U527MmDHau3evSktLXcvw4cOVm5ur0tLSZuFGCuIc+vUUZgNt2LDBstvt1tq1a639+/dbM2bMsBISEqyqqirLsizrjjvusPLz813177//vtWlSxfriSeesMrKyqxFixZZXbt2tfbu3RusIbTJ2/EtXrzYeuedd6zPP//cKikpsSZPnmxFR0dbn3zySbCG0KaTJ09aH330kfXRRx9ZkqwVK1ZYH330kXX48GHLsiwrPz/fuuOOO1z1//M//2PFxMRYDzzwgFVWVmatWrXKioyMtAoLC4M1hDZ5O75f/vKX1muvvWZ9+umn1t69e6377rvPioiIsP70pz8FawhtmjlzphUfH29t3brVqqysdC2nT5921YT7e7AjYwyn92F+fr61bds26+DBg9bHH39s5efnWzabzXr33Xctywr/+fN2fOE0d605/yqqUJlDAk4HPPXUU9bll19uRUVFWSNGjLA++OAD13OjRo2y7rzzTrf6//7v/7b+5V/+xYqKirKuvPJK68033wxwx97xZnyzZ8921fbo0cMaP368tWfPniB07Zlzl0Wfv5wb05133mmNGjWq2TaDBw+2oqKirD59+ljPP/98wPv2lLfje/zxx60rrrjCio6OthITE63Ro0dbW7ZsCU7zHmhpbJLc5iTc34MdGWM4vQ9//OMfWz179rSioqKspKQka8yYMa4Pf8sK//nzdnzhNHetOT/ghMoc2izLsvx7jAgAACCwOAcHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOP8PxTjm0PKgeg3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops.  These should all be within single digits\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()\n",
    "plotThis = False\n",
    "for t in popHDlist:  #seems like for TN, we lost a vtd in converting from unit-based to vtd-based\n",
    "    if abs(HDvPop[t] - HDvtdbasedPop[t]) > 10:\n",
    "        plotThis = True\n",
    "        print(\"t, HDunit-based pop - pop from vtd-based HDs\",t,r3(HDvPop[t] - HDvtdbasedPop[t]) )\n",
    "        plotPoly(tractCP[t].buffer(0.1))\n",
    "if plotThis:\n",
    "    plotPoly(MAP)\n",
    "    print(\"Here are the HD centers with significant pop difference between unit-based and vtd-based HD lists\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for AZ\n",
      "In year/election 2020 Dem and GOP total votes were 1672113 1661655 for a stateGOP of 0.49843\n",
      "There are a total of 1538  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 1161138 1252385 for a stateGOP of 0.5189\n",
      "There are a total of 1538  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for AZ\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv AZ1538RedonepatchedVTDs.csv\n",
      "enter the number of Congr'l districts in the state; e.g. 8 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 1538 drawn districts.  This state has 9 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of Congr'l districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = [ str( tractPopFile['GEOID20'][i]) for i in range(len(tractPopFile['GEOID20'])) ] #force strings to match\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "mergedGEO = [str(mergedDF['GEOID20'][i]) for i in range(len(mergedDF['GEOID20'])) ]  #force strings to match\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedGEO, \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])  #for MA, do GOP-leaning.  Other states, use Dem-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"D\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 1672113 1661655 -10457\n",
      "ensemble : 1671245 1661185 -10059\n",
      "the true and ensemble state GOP leans are 0.49843 0.4984906561766055\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 1538 1538 9\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
